shhh <- suppressPackageStartupMessages # It's a library, so shhh!
shhh(library( mgcv ))
shhh(library(dplyr))
shhh(library(ggplot2))
shhh(library(lme4))
shhh(library(tidymv))
shhh(library(gamlss))
shhh(library(gsubfn))
shhh(library(lmerTest))
shhh(library(tidyverse))
shhh(library(boot))
shhh(library(rsample))
shhh(library(plotrix))
shhh(library(ggrepel))
shhh(library(mgcv))
shhh(library(brms))
shhh(library(bayesplot))
shhh(library(tidyr))
shhh(library(car))
shhh(library(HDInterval))
shhh(library(gridExtra))
shhh(library(posterior))
shhh(library(readxl))
shhh(library(stringr))
shhh(library(loo))
shhh(library(MASS))
shhh(library(hypr))
shhh(library(designr))
shhh(library(afex))
shhh(library(coda))
shhh(library(rstan))
shhh(library(rstantools))
rstan_options(auto_write=TRUE)
options(mc.cores=parallel::detectCores())
rstan_options(auto_write = TRUE)
theme_set(theme_bw())
options(digits=4)
options(scipen=999)
set.seed(444)
file_list <- list.files("/Users/cui/Documents/uzh/PhD/Projects/Russian_Agreement/russian_gender/ref/Eyetracking/", pattern = "*.xlsx", full.names = TRUE)
et_raw <- file_list %>%
lapply(read_excel) %>%
bind_rows()
select_meas <- c("SFD", "total_duration", "gaze_duration", "FPFix", "go_past_time", "FPReg", "RegIn")
et <- et_raw %>%
dplyr::select(IA_LABEL, item, word.id, list, RECORDING_SESSION_LABEL, SFD, IA_DWELL_TIME, IA_FIRST_RUN_DWELL_TIME, IA_FIRST_FIX_PROGRESSIVE, IA_SELECTIVE_REGRESSION_PATH_DURATION, IA_REGRESSION_OUT, IA_REGRESSION_IN, gender_match, part, target_gender, type, Region, condition, ACCURACY, animacy) %>%
rename(
word = IA_LABEL,
item_id = item,
word_nr = word.id,
subj_id = RECORDING_SESSION_LABEL,
total_duration = IA_DWELL_TIME,
gaze_duration = IA_FIRST_RUN_DWELL_TIME,
first_pass_fix = IA_FIRST_FIX_PROGRESSIVE,
go_past_time = IA_SELECTIVE_REGRESSION_PATH_DURATION,
FPReg = IA_REGRESSION_OUT,
RegIn = IA_REGRESSION_IN,
AOI_id = Region
) %>%
filter(subj_id != "russ34") %>% # russ34 has acc 0.6 according to the calculation below.
mutate(
go_past_time = as.numeric(go_past_time),
SFD = if_else(first_pass_fix == 1, SFD, 0),
gaze_duration = if_else(first_pass_fix == 1, gaze_duration, 0),
go_past_time = if_else(first_pass_fix == 1, go_past_time, 0),
) %>%
rename(FPFix = first_pass_fix) %>%
mutate(
FPReg = ifelse(gaze_duration==0, NA, FPReg),
FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
gather(measure, value, select_meas) %>%
mutate(
value = as.numeric(value),
tgt_zero = if_else(measure %in% c("SFD", "gaze_duration", "go_past_time", "total_duration") & value == 0, F, T)) %>%
filter(tgt_zero != F) %>%
dplyr::select(-tgt_zero, -condition) %>%
mutate(item_id = as.factor(item_id),
subj_id = as.factor(subj_id)) %>%
spread(measure, value) %>%
# Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
# We filter outliers only for aesthetic reasons in plotting.
gather(measure, value, c("SFD", "gaze_duration", "go_past_time", "total_duration")) %>%
mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
filter(outlier == FALSE) %>%
dplyr::select(-outlier) %>%
spread(measure, value) %>%
gather(measure, value, select_meas) %>%
mutate(cond = case_when(
target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
)) %>%
# filter(animacy %in% c("Inanim", "inanim")) %>%
dplyr::select(-list, -part, -animacy)
et
# The path to the data
data_path <- "./data/"
data_names <- list.files(data_path)
# Read in the data from each participant and add to the data frame
motr_df <- data.frame()
for(name in data_names){
subj <- gsub("reader_", "", gsub("_reading_measures.csv", "", name))
temp_df <- read.csv(paste0(data_path, "/", name)) %>% mutate(subj_id = subj)
motr_df <- rbind(motr_df, temp_df)
}
motr_df <- motr_df %>% mutate(word_len = nchar(word),
word_length = scale(word_len)[,1]) %>%
group_by(subj_id, item_id) %>%
arrange(subj_id, item_id) %>%
mutate(word_len_pre1 = lag(word_length, n = 1),
word_len_pre2 = lag(word_length, n = 2)) %>%
ungroup()
# Clean the data
motr <- motr_df %>%
# filter(subj_id != 171) %>% # acc = 0.8
filter(! list %in% c(98, 99)) %>% # filter practice and filler items
mutate(skip = ifelse(total_duration==0, 1, 0),
FPReg = ifelse(gaze_duration==0, NA, FPReg),
FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
filter(skip == 0) %>%
gather(measure, value, 18:26) %>%
mutate(tgt_zero = if_else(measure %in% c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration") & value == 0, F, T)) %>%
filter(tgt_zero != F) %>%
dplyr::select(-tgt_zero, -cond_id, -skip, -word_len) %>%
mutate(item_id = as.factor(item_id),
subj_id = as.factor(subj_id)) %>%
spread(measure, value) %>%
# Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
# We filter outliers only for aesthetic reasons in plotting.
gather(measure, value, c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration")) %>%
mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
filter(outlier == FALSE) %>%
dplyr::select(-outlier) %>%
spread(measure, value) %>%
gather(measure, value, 21:29) %>%
mutate(cond = case_when(
target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
)) %>%
dplyr::select(-list, -part, -type_id, -orig_item_number, -case, -animacy, -response_true, -response_chosen) %>%
mutate(word = str_replace_all(word, "\\.", "")) %>%
rowwise() %>%
mutate(log_freq = ifelse(word %in% et_raw$IA_LABEL,
et_raw$lg_frequency[match(word, et_raw$IA_LABEL)],
NA_real_)) %>%
ungroup()
# View(motr)
motr_acc <- motr %>% dplyr::select(item_id, cond, subj_id, correctness) %>%
filter(correctness != 99) %>%
distinct()
motr_acc_summary <- motr_acc %>%
group_by(subj_id) %>%
summarise(mean_correctness = mean(correctness),
sd_correctness = sd(correctness),
count = n())
# only subj_id 171 get acc = 0.8; others all > 0.88 (incl. fillers)
# write.csv(motr_acc_summary, "./stats/correctness_summary.csv", row.names = FALSE)
et_acc <- et %>% dplyr::select(all_of(c("item_id", "subj_id", "cond", "ACCURACY"))) %>%
# filter(ACCURACY != -1) %>%
distinct() %>%
mutate(correctness = as.numeric(unlist(ACCURACY)))
et_acc_summary <- et_acc %>%
group_by(subj_id) %>%
summarise(mean_correctness = mean(correctness),
sd_correctness = sd(correctness),
count = n())
# only subj_id russ34 get acc = 0.6; others all > 0.8 (excl. fillers)
motr_acc_cond <- motr_acc %>%
group_by(cond) %>%
summarise(
mean_correctness = round(mean(correctness), 2),
sd_correctness = round(sd(correctness), 2),
count = n()
)
motr_acc_cond
et_acc_cond <- et_acc %>%
group_by(cond) %>%
summarise(
mean_correctness = round(mean(correctness), 2),
sd_correctness = round(sd(correctness), 2),
count = n()
)
et_acc_cond
Are RTs significantly different between gender-match and gender-mismatch conditions? ==> main effect of grammaticality (gender match or not)
Are RTs different in Masculine versus Feminine sentence conditions? ==> main effect of gender of target word
Are RTs affected by lexical category (whether different lexical categories of the agreeing element will make the processing more difficult or not)? –> ADJ(adj & pre_adj) v.s. VERB ==> main effect of lexical type of sentences.
Are RTs affected by feature matching mechanism(whether agreeing element instantiates internal v.s. external agreement will make a difference in processing difficulty)? –> External (verb & predicative adjective) v.s. Internal (modifying adjective) ==> main effect of syntax type of sentences.
Whether the effect of grammaticality is modulated by lexical category? –> Is the RT difference caused by grammaticality effect affected by the target word being Adj or Verb? ==> interaction between grammaticality and lexical type
Whether the effect of grammaticality depends on the feature matching mechanism of the sentence? –> Is the RT difference caused by grammaticality effect affected by the mechanism being external or internal? ==> interaction between grammaticality and feature matching mechanism
Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between lexical category (Adj v.s. Verb)? ==> 3-way interaction between grammaticality, gender and lexical category
Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between feature matching mechanism (External v.s. Internal)? ==> 3-way interaction between grammaticality, gender and feature matching mechanism
# check conditions
et$cond <- factor(et$cond)
levels(et$cond)
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
motr$cond <- factor(motr$cond)
levels(motr$cond)
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
## sol1
X_H <- matrix(c(1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12, # Intercept
1/6,1/6,1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6, # Main effect of grammaticality
1/6,1/6,1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6, # Main effect of gender
1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8, # Main effect of feature matching
-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8, # Main effect of lexical category
1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6, # gram x gen
1/4,-1/8,-1/8,-1/4,1/8,1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8, # gram x synt
-1/8,1/4,-1/8,1/8,-1/4,1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8, # gram x lex
1/4,-1/8,-1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8,-1/4,1/8,1/8, #gen x synt
-1/8,1/4,-1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8,1/8,-1/4,1/8, # gen x lex
1/2,-1/4,-1/4,-1/2,1/4,1/4,-1/2,1/4,1/4,1/2,-1/4,-1/4, # gram x gen x synt
-1/4,1/2,-1/4,1/4,-1/2,1/4,1/4,-1/2,1/4,-1/4,1/2,-1/4 # gram x gen x lex
), byrow=TRUE, nrow = 12)
# X_H
# rowSums(X_H) # ensure centering
X_C = ginv(X_H)
rownames(X_C) <- c('a','b','c','d','e','f','g','h', 'i', 'j', 'k', 'l')
colnames(X_C) <- c('Int','Gram','Gen','Lex','Synt','Gram_x_Gen','Gram_x_Lex','Gram_x_Synt','Gen_x_Lex','Gen_x_synt','Gram_x_Gen_Lex','Gram_x_Gen_Synt')
X_C_bar <- X_C[,2:ncol(X_C)]
fractions(X_C_bar)
Gram Gen Lex Synt Gram_x_Gen Gram_x_Lex Gram_x_Synt Gen_x_Lex Gen_x_synt Gram_x_Gen_Lex
a 1/2 1/2 2/3 0 1/2 2/3 0 2/3 0 1/3
b 1/2 1/2 0 2/3 1/2 0 2/3 0 2/3 0
c 1/2 1/2 -2/3 -2/3 1/2 -2/3 -2/3 -2/3 -2/3 -1/3
d -1/2 1/2 2/3 0 -1/2 -2/3 0 2/3 0 -1/3
e -1/2 1/2 0 2/3 -1/2 0 -2/3 0 2/3 0
f -1/2 1/2 -2/3 -2/3 -1/2 2/3 2/3 -2/3 -2/3 1/3
g 1/2 -1/2 2/3 0 -1/2 2/3 0 -2/3 0 -1/3
h 1/2 -1/2 0 2/3 -1/2 0 2/3 0 -2/3 0
i 1/2 -1/2 -2/3 -2/3 -1/2 -2/3 -2/3 2/3 2/3 1/3
j -1/2 -1/2 2/3 0 1/2 -2/3 0 -2/3 0 1/3
k -1/2 -1/2 0 2/3 1/2 0 -2/3 0 -2/3 0
l -1/2 -1/2 -2/3 -2/3 1/2 2/3 2/3 2/3 2/3 -1/3
Gram_x_Gen_Synt
a 0
b 1/3
c -1/3
d 0
e -1/3
f 1/3
g 0
h -1/3
i 1/3
j 0
k 1/3
l -1/3
contr_motr <- motr %>%
mutate(
#--------------------- main effects ---------------------
Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality
Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching (a vs pv)
Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (ap vs v)
#--------------------- 2-way interactions ---------------------
Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
#--------------------- 3 way interection ---------------------
Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
) %>% spread(measure, value) #%>%
# # filter(word_nr == 3)
# filter(AOI_id == "R3")
contr_motr
contr_et <- et %>%
mutate(
#--------------------- main effects ---------------------
Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality
Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching (a vs pv)
Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (v vs ap)
#--------------------- 2-way interactions ---------------------
Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
#--------------------- 3 way interection ---------------------
Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
) %>% spread(measure, value) %>%
rename(RegIn_incl = RegIn)
# View(contr_et)
write.csv(contr_et, "./stats/et_reading_measures_contrast_coded.csv", row.names = FALSE)
write.csv(contr_motr, "./stats/motr_reading_measures_contrast_coded.csv", row.names = FALSE)
## sol2 --> try hypr package, also for sanity check
hypothesis_matrix <- hypr(
Gram = (a+b+c+g+h+i)/6 ~ (d+e+f+j+k+l)/6,
Gen = (a+b+c+d+e+f)/6 ~ (g+h+i+j+k+l)/6,
Synt = (a+d+g+j)/4 ~ (b+c+e+f+h+i+k+l)/8,
Lex = (b+e+h+k)/4 ~ (a+c+d+f+g+i+j+l)/8,
# Gram_x_Gen = ((a+b+c)/3-(d+e+f)/3)/2 ~ ((g+h+i)/3-(j+k+l)/3)/2,
Gram_x_Synt = ((e+f+k+l)/4-(d+j)/2)/2 ~ ((b+c+h+i)/4-(a+g)/2)/2 ,
Gram_x_Lex = ((d+f+j+l)/4-(e+k)/2)/2 ~ ((a+c+g+i)/4-(b+h)/2)/2,
# Gen_x_Synt = ((h+i+k+l)/4-(g+j)/2)/2 ~ ((b+c+e+f)/4-(a+d)/2)/2,
# Gen_x_Lex = ((g+i+j+l)/4-(h+k)/2)/2 ~ ((a+c+d+f)/4-(b+e)/2)/2,
Gram_x_Gen_x_Synt = (((h+i)/2-g)-((k+l)/2-j))/2 ~ (((b+c)/2-a)-((e+f)/2-d))/2,
Gram_x_Gen_x_Lex = (((g+i)/2-h)-((j+l)/2-k))/2 ~ (((a+c)/2-b)-((d+f)/2-e))/2
)
# Display the matrix
hypothesis_matrix
hypr object containing 8 null hypotheses:
H0.Gram: 0 = (a + b + c + g + h + i - d - e - f - j - k - l)/6
H0.Gen: 0 = (a + b + c + d + e + f - g - h - i - j - k - l)/6
H0.Synt: 0 = (a + d + g + j - 1/2*b - 1/2*c - 1/2*e - 1/2*f - 1/2*h - 1/2*i - 1/2*k - 1/2*l)/4
H0.Lex: 0 = (b + e + h + k - 1/2*a - 1/2*c - 1/2*d - 1/2*f - 1/2*g - 1/2*i - 1/2*j - 1/2*l)/4
H0.Gram_x_Synt: 0 = (1/2*e + 1/2*f + 1/2*k + 1/2*l - d - j - 1/2*b - 1/2*c - 1/2*h - 1/2*i + a + g)/4
H0.Gram_x_Lex: 0 = (1/2*d + 1/2*f + 1/2*j + 1/2*l - e - k - 1/2*a - 1/2*c - 1/2*g - 1/2*i + b + h)/4
H0.Gram_x_Gen_x_Synt: 0 = (1/2*h + 1/2*i - g - 1/2*k - 1/2*l + j - 1/2*b - 1/2*c + a + 1/2*e + 1/2*f - d)/2
H0.Gram_x_Gen_x_Lex: 0 = (1/2*g + 1/2*i - h - 1/2*j - 1/2*l + k - 1/2*a - 1/2*c + b + 1/2*d + 1/2*f - e)/2
Call:
hypr(Gram = ~1/6 * a + 1/6 * b + 1/6 * c + 1/6 * g + 1/6 * h +
1/6 * i - 1/6 * d - 1/6 * e - 1/6 * f - 1/6 * j - 1/6 * k -
1/6 * l, Gen = ~1/6 * a + 1/6 * b + 1/6 * c + 1/6 * d + 1/6 *
e + 1/6 * f - 1/6 * g - 1/6 * h - 1/6 * i - 1/6 * j - 1/6 *
k - 1/6 * l, Synt = ~1/4 * a + 1/4 * d + 1/4 * g + 1/4 *
j - 1/8 * b - 1/8 * c - 1/8 * e - 1/8 * f - 1/8 * h - 1/8 *
i - 1/8 * k - 1/8 * l, Lex = ~1/4 * b + 1/4 * e + 1/4 * h +
1/4 * k - 1/8 * a - 1/8 * c - 1/8 * d - 1/8 * f - 1/8 * g -
1/8 * i - 1/8 * j - 1/8 * l, Gram_x_Synt = ~1/8 * e + 1/8 *
f + 1/8 * k + 1/8 * l - 1/4 * d - 1/4 * j - 1/8 * b - 1/8 *
c - 1/8 * h - 1/8 * i + 1/4 * a + 1/4 * g, Gram_x_Lex = ~1/8 *
d + 1/8 * f + 1/8 * j + 1/8 * l - 1/4 * e - 1/4 * k - 1/8 *
a - 1/8 * c - 1/8 * g - 1/8 * i + 1/4 * b + 1/4 * h, Gram_x_Gen_x_Synt = ~1/4 *
h + 1/4 * i - 1/2 * g - 1/4 * k - 1/4 * l + 1/2 * j - 1/4 *
b - 1/4 * c + 1/2 * a + 1/4 * e + 1/4 * f - 1/2 * d, Gram_x_Gen_x_Lex = ~1/4 *
g + 1/4 * i - 1/2 * h - 1/4 * j - 1/4 * l + 1/2 * k - 1/4 *
a - 1/4 * c + 1/2 * b + 1/4 * d + 1/4 * f - 1/2 * e, levels = c("a",
"b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"))
Hypothesis matrix (transposed):
Gram Gen Synt Lex Gram_x_Synt Gram_x_Lex Gram_x_Gen_x_Synt Gram_x_Gen_x_Lex
a 1/6 1/6 1/4 -1/8 1/4 -1/8 1/2 -1/4
b 1/6 1/6 -1/8 1/4 -1/8 1/4 -1/4 1/2
c 1/6 1/6 -1/8 -1/8 -1/8 -1/8 -1/4 -1/4
d -1/6 1/6 1/4 -1/8 -1/4 1/8 -1/2 1/4
e -1/6 1/6 -1/8 1/4 1/8 -1/4 1/4 -1/2
f -1/6 1/6 -1/8 -1/8 1/8 1/8 1/4 1/4
g 1/6 -1/6 1/4 -1/8 1/4 -1/8 -1/2 1/4
h 1/6 -1/6 -1/8 1/4 -1/8 1/4 1/4 -1/2
i 1/6 -1/6 -1/8 -1/8 -1/8 -1/8 1/4 1/4
j -1/6 -1/6 1/4 -1/8 -1/4 1/8 1/2 -1/4
k -1/6 -1/6 -1/8 1/4 1/8 -1/4 -1/4 1/2
l -1/6 -1/6 -1/8 -1/8 1/8 1/8 -1/4 -1/4
Contrast matrix:
Gram Gen Synt Lex Gram_x_Synt Gram_x_Lex Gram_x_Gen_x_Synt Gram_x_Gen_x_Lex
a 1/2 1/2 2/3 0 2/3 0 1/3 0
b 1/2 1/2 0 2/3 0 2/3 0 1/3
c 1/2 1/2 -2/3 -2/3 -2/3 -2/3 -1/3 -1/3
d -1/2 1/2 2/3 0 -2/3 0 -1/3 0
e -1/2 1/2 0 2/3 0 -2/3 0 -1/3
f -1/2 1/2 -2/3 -2/3 2/3 2/3 1/3 1/3
g 1/2 -1/2 2/3 0 2/3 0 -1/3 0
h 1/2 -1/2 0 2/3 0 2/3 0 -1/3
i 1/2 -1/2 -2/3 -2/3 -2/3 -2/3 1/3 1/3
j -1/2 -1/2 2/3 0 -2/3 0 1/3 0
k -1/2 -1/2 0 2/3 0 -2/3 0 1/3
l -1/2 -1/2 -2/3 -2/3 2/3 2/3 -1/3 -1/3
stats_freq = data.frame()
# regions = c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions = c("R3")
measure_types = c("gaze_duration", "go_past_time", "total_duration",
"FPReg", "RegIn_incl"
)
for (meth in methods) {
for (region in regions) {
for (meas in measure_types){
print(paste("Fitting model for:", meas, "in Region:", region))
if (meas %in% c("first_duration", "gaze_duration", "go_past_time", "total_duration")){
if (meth == "motr") {
model <- contr_motr %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]])) %>%
lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 + Gram | subj_id)")),
data = ., REML = F)
} else {
model <- contr_et %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]])) %>%
lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 + Gram | subj_id)")),
data = ., REML = F)
}
coefs <- summary(model)$coefficients
temp_results <- data.frame(
method = meth,
region = region,
measure = meas,
beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt",
"b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
bval = coefs[, "Estimate"],
pval = coefs[, "Pr(>|t|)"]
)
}else{
if (meth == "motr") {
model <- contr_motr %>% filter(!is.na(.data[[meas]])) %>%
glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 | subj_id)")),
data = ., family=binomial(link = "logit"))
} else{
model <- contr_et %>% filter(!is.na(.data[[meas]])) %>%
glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 | subj_id)")),
data = ., family=binomial(link = "logit"))
}
coefs <- summary(model)$coefficients
temp_results <- data.frame(
method = meth,
region = region,
measure = meas,
beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt",
"b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
bval = coefs[, "Estimate"],
pval = coefs[, "Pr(>|z|)"]
)
}
stats_freq = rbind(stats_freq, temp_results)
}
}
}
stats_freq <- stats_freq %>%
mutate(sig = case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
pval < 0.1 ~ ".",
TRUE ~ ""
))
# View(stats_freq)
# write.csv(stats_freq, "./stats/stats_motr_et_lmer.csv", row.names = FALSE)
createStanData <-function(d, dv,form){
subj <- as.integer(factor(d$subj_id))
N_subj <- length(unique(subj))
item <- as.integer(factor(d$item_id))
N_items <- length(unique(item))
X <- unname(model.matrix(form, d))
attr(X, which="assign") <- NULL
stanData <- list(N = nrow(X),
P = ncol(X),
n_u = ncol(X),
n_w = 3,
X = X,
Z_u = X,
Z_w = X[, 1:3, drop = FALSE], # only by-item random intercept, Gram, Gen
J = N_subj,
K = N_items,
dv = dv,
subj = subj,
item = item)
stanData
}
# note: this chunk takes time to run ~ 1 hour for one region
# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# measure_types <- c("go_past_time")
for (meth in methods) {
for (region in regions) {
for (meas in measure_types) {
print(paste("Fitting Bayesian model for:", meas, "in Region:", region))
if (meth == "motr"){
# Filter data for current region and non-missing measure
temp <- contr_motr %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]]))
} else {
temp <- contr_et %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]]))
}
# binary dv
if (meas %in% c("FPReg", "RegIn_incl")) {
stan_data <- createStanData(
d = temp,
form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"),
dv = temp[[meas]]
)
stan_model_file <- "stan/Model_binary.stan"
} else {
# For other measures, use the default formulas and models
stan_data <- createStanData(
d = temp,
form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"),
dv = temp[[meas]]
)
stan_model_file <- "stan/Model_RT.stan"
}
# Fit model
stan_model <- stan(
file = stan_model_file,
data = stan_data,
iter = 4000,
chains = 4,
control = list(adapt_delta = 0.99)
)
# Save model output
model_save_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
saveRDS(stan_model, file = model_save_path)
}
}
}
# change xx.rds to other models to check them
region <- "R3"
meas <- "go_past_time"
model_path <- paste0("models/et_", meas, "_", region, ".rds")
m1_gd <- readRDS(model_path)
summary(m1_gd)
$summary
mean se_mean sd 2.5% 25% 50%
beta[1] 5.7137276 0.00116026 0.04449 5.6255388 5.68434020 5.713520936
beta[2] 0.1278191 0.00028141 0.02518 0.0767242 0.11149445 0.127758066
beta[3] -0.0110824 0.00022626 0.02377 -0.0582467 -0.02680162 -0.011386977
beta[4] -0.0512615 0.00064983 0.03962 -0.1301893 -0.07721791 -0.051252348
beta[5] -0.1106493 0.00063248 0.04011 -0.1865604 -0.13833594 -0.111422194
beta[6] 0.0117341 0.00022192 0.02214 -0.0312682 -0.00309903 0.011362633
beta[7] -0.0248344 0.00021342 0.02185 -0.0669826 -0.03937759 -0.024796391
beta[8] -0.0045650 0.00035250 0.03875 -0.0806718 -0.03032326 -0.004716087
beta[9] -0.0206183 0.00033778 0.03779 -0.0954384 -0.04559835 -0.020265754
sigma_e 0.3921495 0.00007697 0.00720 0.3785931 0.38721123 0.391947750
sigma_u[1] 0.2529664 0.00075530 0.03213 0.1990411 0.23037704 0.249860362
sigma_u[2] 0.1053344 0.00069806 0.03168 0.0397446 0.08587754 0.105457676
sigma_u[3] 0.0210309 0.00019291 0.01639 0.0008581 0.00809264 0.017342948
sigma_u[4] 0.0264723 0.00028287 0.01993 0.0011474 0.01047744 0.022516511
sigma_u[5] 0.0370721 0.00042782 0.02568 0.0017461 0.01621900 0.033231597
sigma_u[6] 0.0402110 0.00044760 0.02594 0.0019864 0.01902661 0.037557373
sigma_u[7] 0.0290649 0.00033137 0.02160 0.0011088 0.01168992 0.025014587
sigma_u[8] 0.0700658 0.00078836 0.04788 0.0032722 0.03151649 0.062843893
sigma_u[9] 0.0504653 0.00051471 0.03727 0.0022300 0.02067267 0.043085240
sigma_w[1] 0.1215831 0.00030786 0.01715 0.0912811 0.10935583 0.120383139
sigma_w[2] 0.0455099 0.00049511 0.02889 0.0019983 0.02207275 0.043196198
sigma_w[3] 0.0944788 0.00079417 0.03456 0.0181357 0.07367294 0.096009703
L_u[1,1] 1.0000000 NaN 0.00000 1.0000000 1.00000000 1.000000000
L_u[1,2] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,3] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,4] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,1] 0.1552419 0.00215168 0.19956 -0.2491587 0.02349424 0.160363298
L_u[2,2] 0.9665586 0.00056708 0.04296 0.8470914 0.95302364 0.983105894
L_u[2,3] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,4] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,1] -0.0359758 0.00215963 0.28366 -0.5744168 -0.23447634 -0.039973263
L_u[3,2] 0.0209408 0.00220376 0.28972 -0.5337836 -0.18367019 0.025472455
L_u[3,3] 0.9094959 0.00155353 0.08187 0.6979178 0.87061704 0.932317628
L_u[3,4] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,1] -0.0132876 0.00226170 0.27396 -0.5313123 -0.20401133 -0.015092069
L_u[4,2] 0.0077878 0.00223027 0.28129 -0.5460384 -0.19074487 0.009194853
L_u[4,3] -0.0089796 0.00237496 0.29254 -0.5614333 -0.22141558 -0.008826501
L_u[4,4] 0.8660699 0.00172079 0.09939 0.6193810 0.81233656 0.888783980
L_u[4,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,1] -0.0314604 0.00218045 0.25860 -0.5123615 -0.21454363 -0.034753994
L_u[5,2] -0.0077931 0.00252056 0.27874 -0.5429627 -0.20973779 -0.007226191
L_u[5,3] -0.0151788 0.00290837 0.29238 -0.5705059 -0.22303423 -0.019483430
L_u[5,4] -0.0109113 0.00301111 0.29182 -0.5716054 -0.21879207 -0.011878085
L_u[5,5] 0.8190122 0.00177359 0.11243 0.5472738 0.75386858 0.839161068
L_u[5,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[6,1] 0.1699310 0.00241785 0.26811 -0.3915943 -0.00623231 0.191516332
L_u[6,2] -0.1232831 0.00289328 0.27899 -0.6195553 -0.32823579 -0.136666671
L_u[6,3] -0.0056129 0.00279400 0.28560 -0.5533982 -0.20598338 -0.006156572
L_u[6,4] 0.0049108 0.00271092 0.28134 -0.5368213 -0.19549189 0.006578870
L_u[6,5] -0.0144534 0.00279867 0.28122 -0.5379807 -0.21668980 -0.015900686
L_u[6,6] 0.7407234 0.00212625 0.13238 0.4467269 0.65521301 0.755071546
L_u[6,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[6,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[6,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[7,1] 0.0225342 0.00231310 0.27254 -0.5055719 -0.16540892 0.020566509
L_u[7,2] -0.0173667 0.00249217 0.28622 -0.5641868 -0.22264554 -0.017735141
L_u[7,3] -0.0056213 0.00253197 0.29154 -0.5626096 -0.21212310 -0.008479741
L_u[7,4] -0.0194204 0.00249284 0.29218 -0.5691138 -0.22475292 -0.018512991
L_u[7,5] -0.0115889 0.00259252 0.29089 -0.5593560 -0.22242404 -0.018337900
L_u[7,6] -0.0505733 0.00261831 0.29493 -0.6122371 -0.26017809 -0.055786325
L_u[7,7] 0.6910385 0.00254748 0.14303 0.3766181 0.59996601 0.706481272
L_u[7,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[7,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[8,1] -0.0187178 0.00195627 0.26327 -0.5253724 -0.20407320 -0.021178999
L_u[8,2] 0.1322966 0.00264911 0.27673 -0.4278802 -0.05991167 0.144514676
L_u[8,3] -0.0101853 0.00282423 0.28970 -0.5544915 -0.21774771 -0.010900006
L_u[8,4] -0.0136163 0.00278354 0.28947 -0.5625898 -0.22088930 -0.012913273
L_u[8,5] 0.0316614 0.00282775 0.28592 -0.5346124 -0.16834414 0.037165430
L_u[8,6] -0.0517191 0.00290483 0.28517 -0.5878236 -0.25370805 -0.060174047
L_u[8,7] -0.0100569 0.00287979 0.29450 -0.5785394 -0.22076021 -0.007100980
L_u[8,8] 0.6259864 0.00233419 0.15127 0.3118438 0.52113413 0.634421109
L_u[8,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[9,1] 0.1099632 0.00222724 0.28671 -0.4739303 -0.08856047 0.124010155
L_u[9,2] -0.0142182 0.00250760 0.27879 -0.5484632 -0.21229502 -0.012128088
L_u[9,3] -0.0093962 0.00249653 0.28988 -0.5625705 -0.21389277 -0.008372280
L_u[9,4] 0.0020518 0.00242878 0.28715 -0.5547114 -0.19743645 0.003038669
L_u[9,5] -0.0098456 0.00233232 0.28797 -0.5568942 -0.21547740 -0.011699018
L_u[9,6] 0.0285579 0.00254097 0.28480 -0.5219290 -0.17183222 0.032007504
75% 97.5% n_eff Rhat
beta[1] 5.7436262 5.799604 1470 1.0003
beta[2] 0.1448759 0.176214 8008 0.9996
beta[3] 0.0046509 0.035814 11033 1.0001
beta[4] -0.0250399 0.026628 3717 1.0013
beta[5] -0.0836334 -0.030294 4021 1.0009
beta[6] 0.0267339 0.055618 9957 0.9996
beta[7] -0.0104658 0.018842 10482 1.0000
beta[8] 0.0210333 0.072646 12087 0.9997
beta[9] 0.0048688 0.054204 12517 0.9997
sigma_e 0.3968681 0.406687 8750 0.9997
sigma_u[1] 0.2721241 0.323789 1810 1.0018
sigma_u[2] 0.1258091 0.166874 2060 1.0017
sigma_u[3] 0.0303824 0.061450 7221 1.0000
sigma_u[4] 0.0382020 0.073839 4964 1.0011
sigma_u[5] 0.0539737 0.094945 3603 1.0012
sigma_u[6] 0.0577973 0.095719 3358 1.0008
sigma_u[7] 0.0421030 0.079998 4249 1.0008
sigma_u[8] 0.1013385 0.175856 3688 1.0002
sigma_u[9] 0.0726753 0.140210 5242 1.0000
sigma_w[1] 0.1323689 0.157345 3104 1.0003
sigma_w[2] 0.0645675 0.107832 3406 1.0002
sigma_w[3] 0.1175534 0.159070 1893 1.0013
L_u[1,1] 1.0000000 1.000000 NaN NaN
L_u[1,2] 0.0000000 0.000000 NaN NaN
L_u[1,3] 0.0000000 0.000000 NaN NaN
L_u[1,4] 0.0000000 0.000000 NaN NaN
L_u[1,5] 0.0000000 0.000000 NaN NaN
L_u[1,6] 0.0000000 0.000000 NaN NaN
L_u[1,7] 0.0000000 0.000000 NaN NaN
L_u[1,8] 0.0000000 0.000000 NaN NaN
L_u[1,9] 0.0000000 0.000000 NaN NaN
L_u[2,1] 0.2944338 0.530669 8602 0.9999
L_u[2,2] 0.9963201 0.999966 5740 0.9998
L_u[2,3] 0.0000000 0.000000 NaN NaN
L_u[2,4] 0.0000000 0.000000 NaN NaN
L_u[2,5] 0.0000000 0.000000 NaN NaN
L_u[2,6] 0.0000000 0.000000 NaN NaN
L_u[2,7] 0.0000000 0.000000 NaN NaN
L_u[2,8] 0.0000000 0.000000 NaN NaN
L_u[2,9] 0.0000000 0.000000 NaN NaN
L_u[3,1] 0.1630931 0.519153 17252 0.9997
L_u[3,2] 0.2290743 0.567851 17284 0.9999
L_u[3,3] 0.9724391 0.997542 2777 1.0012
L_u[3,4] 0.0000000 0.000000 NaN NaN
L_u[3,5] 0.0000000 0.000000 NaN NaN
L_u[3,6] 0.0000000 0.000000 NaN NaN
L_u[3,7] 0.0000000 0.000000 NaN NaN
L_u[3,8] 0.0000000 0.000000 NaN NaN
L_u[3,9] 0.0000000 0.000000 NaN NaN
L_u[4,1] 0.1756006 0.525587 14672 0.9997
L_u[4,2] 0.2095263 0.541930 15908 0.9998
L_u[4,3] 0.2014094 0.548018 15173 0.9998
L_u[4,4] 0.9417143 0.990184 3336 1.0014
L_u[4,5] 0.0000000 0.000000 NaN NaN
L_u[4,6] 0.0000000 0.000000 NaN NaN
L_u[4,7] 0.0000000 0.000000 NaN NaN
L_u[4,8] 0.0000000 0.000000 NaN NaN
L_u[4,9] 0.0000000 0.000000 NaN NaN
L_u[5,1] 0.1442983 0.482115 14066 0.9996
L_u[5,2] 0.1927303 0.520266 12229 0.9998
L_u[5,3] 0.1934844 0.552053 10107 0.9999
L_u[5,4] 0.1971026 0.546213 9393 0.9998
L_u[5,5] 0.9053139 0.975247 4019 1.0011
L_u[5,6] 0.0000000 0.000000 NaN NaN
L_u[5,7] 0.0000000 0.000000 NaN NaN
L_u[5,8] 0.0000000 0.000000 NaN NaN
L_u[5,9] 0.0000000 0.000000 NaN NaN
L_u[6,1] 0.3670603 0.638058 12296 0.9997
L_u[6,2] 0.0676310 0.456796 9298 0.9996
L_u[6,3] 0.1970512 0.538322 10449 0.9997
L_u[6,4] 0.2023692 0.545344 10770 0.9998
L_u[6,5] 0.1868490 0.533895 10097 1.0000
L_u[6,6] 0.8422310 0.945662 3876 1.0002
L_u[6,7] 0.0000000 0.000000 NaN NaN
L_u[6,8] 0.0000000 0.000000 NaN NaN
L_u[6,9] 0.0000000 0.000000 NaN NaN
L_u[7,1] 0.2159248 0.542306 13882 0.9998
L_u[7,2] 0.1828129 0.539232 13190 0.9997
L_u[7,3] 0.1994012 0.561794 13258 1.0000
L_u[7,4] 0.1858244 0.548850 13738 1.0004
L_u[7,5] 0.2011992 0.556384 12590 0.9999
L_u[7,6] 0.1578710 0.521280 12688 0.9996
L_u[7,7] 0.7992628 0.921234 3152 1.0006
L_u[7,8] 0.0000000 0.000000 NaN NaN
L_u[7,9] 0.0000000 0.000000 NaN NaN
L_u[8,1] 0.1654984 0.495741 18111 0.9999
L_u[8,2] 0.3357437 0.630349 10912 0.9996
L_u[8,3] 0.1925911 0.550140 10522 0.9999
L_u[8,4] 0.1899936 0.551566 10815 0.9997
L_u[8,5] 0.2381390 0.570667 10224 0.9996
L_u[8,6] 0.1454781 0.514621 9637 0.9998
L_u[8,7] 0.2009850 0.547276 10458 1.0004
L_u[8,8] 0.7395249 0.885437 4200 1.0004
L_u[8,9] 0.0000000 0.000000 NaN NaN
L_u[9,1] 0.3205461 0.624845 16572 0.9996
L_u[9,2] 0.1779917 0.523988 12361 0.9998
L_u[9,3] 0.1999384 0.543844 13482 0.9999
L_u[9,4] 0.2034316 0.545934 13978 0.9996
L_u[9,5] 0.1963436 0.541883 15245 0.9998
L_u[9,6] 0.2311829 0.568936 12563 0.9996
[ reached getOption("max.print") -- omitted 2782 rows ]
$c_summary
, , chains = chain:1
stats
parameter mean sd 2.5% 25% 50% 75%
beta[1] 5.71313764 0.043766 5.62739684 5.68300285 5.71254219 5.7431008
beta[2] 0.12791934 0.024950 0.07498792 0.11203706 0.12819914 0.1451459
beta[3] -0.01112213 0.023176 -0.05676759 -0.02666853 -0.01110475 0.0036782
beta[4] -0.04960346 0.039123 -0.12849671 -0.07545078 -0.04963835 -0.0238676
beta[5] -0.11217025 0.040447 -0.18641035 -0.14051785 -0.11348946 -0.0852989
beta[6] 0.01161017 0.022350 -0.03313501 -0.00338282 0.01135946 0.0266355
beta[7] -0.02447366 0.021900 -0.06722754 -0.03915830 -0.02470305 -0.0099852
beta[8] -0.00462087 0.037926 -0.07997108 -0.03014442 -0.00499958 0.0209451
beta[9] -0.02058520 0.036898 -0.09351357 -0.04481068 -0.02012587 0.0039586
sigma_e 0.39207544 0.007006 0.37895110 0.38729379 0.39194511 0.3966504
sigma_u[1] 0.25341894 0.031375 0.19717351 0.23161912 0.25047438 0.2727886
sigma_u[2] 0.10676070 0.032429 0.03871591 0.08727580 0.10591218 0.1271076
sigma_u[3] 0.02093646 0.016634 0.00092584 0.00786386 0.01684107 0.0301250
sigma_u[4] 0.02562677 0.019635 0.00120941 0.01004256 0.02144569 0.0369157
sigma_u[5] 0.03828180 0.026508 0.00150926 0.01655898 0.03422369 0.0559599
sigma_u[6] 0.04100796 0.026239 0.00217504 0.01966322 0.03848443 0.0588815
sigma_u[7] 0.02906976 0.022120 0.00086479 0.01126423 0.02495456 0.0421510
sigma_u[8] 0.07094983 0.047754 0.00369507 0.03224510 0.06500220 0.1023060
sigma_u[9] 0.05067299 0.037773 0.00215575 0.02084686 0.04349936 0.0727264
sigma_w[1] 0.12096332 0.017302 0.09114024 0.10883240 0.11981586 0.1318524
sigma_w[2] 0.04583392 0.029175 0.00206103 0.02178636 0.04395741 0.0649803
sigma_w[3] 0.09328626 0.034371 0.02019346 0.07267751 0.09430597 0.1161593
L_u[1,1] 1.00000000 0.000000 1.00000000 1.00000000 1.00000000 1.0000000
L_u[1,2] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,4] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,1] 0.15691221 0.198403 -0.24633552 0.02057949 0.16170754 0.2921020
L_u[2,2] 0.96650224 0.043690 0.83861601 0.95379683 0.98293134 0.9962636
L_u[2,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,4] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,1] -0.03815958 0.280535 -0.57738854 -0.23391465 -0.04326842 0.1580817
L_u[3,2] 0.02327923 0.287158 -0.54561289 -0.18194694 0.02724731 0.2305909
L_u[3,3] 0.91135128 0.079781 0.70215181 0.87428239 0.93284365 0.9724314
L_u[3,4] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,1] -0.01088570 0.274922 -0.52293787 -0.20278968 -0.01841786 0.1809470
L_u[4,2] 0.00237729 0.286602 -0.55596744 -0.20295404 0.00247701 0.2057367
L_u[4,3] -0.00613867 0.282614 -0.54812308 -0.20956123 -0.01044375 0.1962542
L_u[4,4] 0.86780109 0.096383 0.62852262 0.81271087 0.89242124 0.9401862
L_u[4,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,1] -0.03311159 0.263181 -0.52491811 -0.21878930 -0.04167821 0.1418992
L_u[5,2] -0.00799661 0.277738 -0.52568994 -0.21851840 -0.01131838 0.1935469
L_u[5,3] -0.01144692 0.287777 -0.54902437 -0.22284774 -0.01082654 0.2035603
L_u[5,4] -0.00584091 0.278193 -0.53964344 -0.20364515 -0.01745674 0.1892421
L_u[5,5] 0.82493006 0.108234 0.55566337 0.76283284 0.84416201 0.9079595
L_u[5,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[6,1] 0.16956210 0.267689 -0.37672018 -0.01220813 0.18580735 0.3721858
L_u[6,2] -0.12543769 0.282335 -0.64196700 -0.32807746 -0.13682650 0.0685792
L_u[6,3] -0.00459167 0.284918 -0.54725398 -0.20795335 -0.00874155 0.1976958
L_u[6,4] 0.00057064 0.278315 -0.52223829 -0.20129768 0.00809006 0.1982392
L_u[6,5] -0.01470836 0.281455 -0.52534619 -0.21710368 -0.02559055 0.1899161
L_u[6,6] 0.74015935 0.135720 0.44082532 0.65320809 0.75746274 0.8435301
L_u[6,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[6,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[6,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[7,1] 0.02223804 0.286393 -0.54361721 -0.17311508 0.01798013 0.2284809
L_u[7,2] -0.02273791 0.284678 -0.54475863 -0.23416284 -0.03320993 0.1806743
L_u[7,3] 0.00483081 0.290280 -0.54724821 -0.20213406 0.00165665 0.2087496
L_u[7,4] -0.00923222 0.286111 -0.54747484 -0.20349797 -0.00614642 0.1895690
L_u[7,5] -0.00262984 0.287465 -0.55387225 -0.20618379 -0.00880802 0.2087780
L_u[7,6] -0.05041789 0.296039 -0.61400879 -0.26902257 -0.04725425 0.1596983
L_u[7,7] 0.69089174 0.140711 0.39227860 0.59995325 0.70340152 0.7983615
L_u[7,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[7,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[8,1] -0.01285382 0.261531 -0.53211650 -0.18674808 -0.01671620 0.1590671
L_u[8,2] 0.12842828 0.273342 -0.42672633 -0.06064058 0.13308968 0.3284855
L_u[8,3] -0.01337892 0.293813 -0.56139709 -0.22236081 -0.01603426 0.1902357
L_u[8,4] -0.00745980 0.293162 -0.57102597 -0.22307292 -0.00792881 0.1930580
L_u[8,5] 0.03277845 0.284400 -0.53456405 -0.16355298 0.03928224 0.2337294
L_u[8,6] -0.06042370 0.280563 -0.56892506 -0.26402724 -0.06592505 0.1350431
L_u[8,7] -0.00677262 0.294891 -0.59025188 -0.21309697 0.00582087 0.2015930
L_u[8,8] 0.62771498 0.150523 0.32329845 0.51563223 0.63997883 0.7387466
L_u[8,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[9,1] 0.10686680 0.293106 -0.48405733 -0.09298402 0.11863457 0.3227717
L_u[9,2] -0.01084245 0.282857 -0.53992432 -0.21696212 -0.00391492 0.1835020
L_u[9,3] -0.00891006 0.291879 -0.54734544 -0.21832521 -0.01351539 0.2004057
L_u[9,4] 0.00069897 0.287099 -0.54085698 -0.20163435 -0.00395903 0.2065248
L_u[9,5] -0.01175079 0.284262 -0.55028206 -0.20846445 -0.01642432 0.1832899
L_u[9,6] 0.02509193 0.283468 -0.52129200 -0.17244766 0.02640681 0.2266400
L_u[9,7] -0.00357695 0.297996 -0.58317961 -0.21948714 0.00082669 0.2097211
L_u[9,8] -0.01473812 0.286922 -0.57422429 -0.21179191 -0.01993437 0.1876050
L_u[9,9] 0.54346818 0.162940 0.21563928 0.43077542 0.55047447 0.6643300
L_w[1,1] 1.00000000 0.000000 1.00000000 1.00000000 1.00000000 1.0000000
L_w[1,2] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_w[1,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_w[2,1] -0.30401238 0.327826 -0.81357081 -0.54842820 -0.34794190 -0.1036936
L_w[2,2] 0.88657918 0.118932 0.58143246 0.82790983 0.92729917 0.9779800
L_w[2,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_w[3,1] 0.09866434 0.284537 -0.49200055 -0.09506462 0.11150080 0.2962814
L_w[3,2] 0.06968991 0.416688 -0.74774712 -0.23383831 0.08445145 0.3885910
L_w[3,3] 0.84291921 0.142981 0.46238734 0.77594717 0.88659776 0.9503859
z_u[1,1] -0.58836305 0.281761 -1.16452379 -0.77433173 -0.59517008 -0.3933977
z_u[1,2] -0.19810587 0.760095 -1.73080437 -0.70680533 -0.20447592 0.2908181
z_u[1,3] 0.05451835 0.965841 -1.81830564 -0.63292057 0.06102908 0.7309231
z_u[1,4] -0.06290138 0.946548 -1.85754943 -0.71735266 -0.06254379 0.5815496
z_u[1,5] -0.02408568 0.988924 -2.00436622 -0.68003504 -0.02460154 0.6140643
z_u[1,6] -0.03081845 0.971008 -1.95086607 -0.65307566 -0.03438576 0.5670417
z_u[1,7] 0.00815684 0.978331 -1.86808703 -0.67652457 -0.00754877 0.6877695
z_u[1,8] -0.16442090 0.976689 -2.09053011 -0.85709358 -0.16786003 0.5090049
z_u[1,9] -0.12258556 0.980552 -1.99227901 -0.78296841 -0.13381940 0.5390079
z_u[2,1] -0.85130600 0.311091 -1.46355826 -1.05503372 -0.84438152 -0.6335456
z_u[2,2] 0.41276530 0.774793 -1.17670203 -0.07002896 0.41045513 0.9031758
z_u[2,3] -0.09570116 0.993574 -2.00761788 -0.80007892 -0.08334085 0.5836216
z_u[2,4] -0.09015756 0.943493 -1.90553870 -0.74319352 -0.09933205 0.5593682
z_u[2,5] 0.02767682 0.935284 -1.85522397 -0.63726149 0.03898061 0.6269704
z_u[2,6] -0.17627449 0.942760 -1.94560330 -0.80124044 -0.19876300 0.4672770
z_u[2,7] 0.01783762 0.956114 -1.88396448 -0.60129251 0.03179733 0.6150744
z_u[2,8] -0.07304053 0.957596 -1.88858379 -0.72882270 -0.06598202 0.5688490
z_u[2,9] 0.04326736 1.009585 -1.88559130 -0.65605537 0.02488998 0.7591116
z_u[3,1] 0.25445523 0.273022 -0.29203265 0.07002352 0.25122544 0.4317084
z_u[3,2] -0.13891772 0.777111 -1.66120185 -0.66336185 -0.14096350 0.3946285
z_u[3,3] 0.23215544 1.022291 -1.79390399 -0.47038038 0.22885645 0.9363247
z_u[3,4] 0.05659148 0.961902 -1.81698240 -0.56878359 0.05211163 0.7230671
z_u[3,5] -0.35135651 1.020955 -2.34443886 -1.01799987 -0.34544883 0.3181704
z_u[3,6] -0.27131848 0.982458 -2.24011411 -0.92586297 -0.27164009 0.4090866
z_u[3,7] 0.21244089 0.967088 -1.69067176 -0.44610883 0.20609954 0.8708612
z_u[3,8] -0.10754544 0.985513 -2.03641887 -0.78073117 -0.09562434 0.5852269
z_u[3,9] -0.10414296 0.996398 -2.04179143 -0.75452794 -0.10650734 0.5510401
z_u[4,1] 0.96171463 0.293592 0.38699449 0.76344241 0.96441645 1.1553424
z_u[4,2] 0.65782315 0.783633 -0.91940016 0.12884452 0.67974249 1.1961863
z_u[4,3] -0.16766907 0.967897 -2.09135456 -0.84537292 -0.18184419 0.5307259
stats
parameter 97.5%
beta[1] 5.8011956
beta[2] 0.1759481
beta[3] 0.0348597
beta[4] 0.0272101
beta[5] -0.0275612
beta[6] 0.0541748
beta[7] 0.0200544
beta[8] 0.0709304
beta[9] 0.0535641
sigma_e 0.4055103
sigma_u[1] 0.3225137
sigma_u[2] 0.1694784
sigma_u[3] 0.0617176
sigma_u[4] 0.0735970
sigma_u[5] 0.0972911
sigma_u[6] 0.0975369
sigma_u[7] 0.0812909
sigma_u[8] 0.1732239
sigma_u[9] 0.1417590
sigma_w[1] 0.1575509
sigma_w[2] 0.1074995
sigma_w[3] 0.1590697
L_u[1,1] 1.0000000
L_u[1,2] 0.0000000
L_u[1,3] 0.0000000
L_u[1,4] 0.0000000
L_u[1,5] 0.0000000
L_u[1,6] 0.0000000
L_u[1,7] 0.0000000
L_u[1,8] 0.0000000
L_u[1,9] 0.0000000
L_u[2,1] 0.5447230
L_u[2,2] 0.9999672
L_u[2,3] 0.0000000
L_u[2,4] 0.0000000
L_u[2,5] 0.0000000
L_u[2,6] 0.0000000
L_u[2,7] 0.0000000
L_u[2,8] 0.0000000
L_u[2,9] 0.0000000
L_u[3,1] 0.5158774
L_u[3,2] 0.5456324
L_u[3,3] 0.9978860
L_u[3,4] 0.0000000
L_u[3,5] 0.0000000
L_u[3,6] 0.0000000
L_u[3,7] 0.0000000
L_u[3,8] 0.0000000
L_u[3,9] 0.0000000
L_u[4,1] 0.5196079
L_u[4,2] 0.5413704
L_u[4,3] 0.5525111
L_u[4,4] 0.9898307
L_u[4,5] 0.0000000
L_u[4,6] 0.0000000
L_u[4,7] 0.0000000
L_u[4,8] 0.0000000
L_u[4,9] 0.0000000
L_u[5,1] 0.4876544
L_u[5,2] 0.5105310
L_u[5,3] 0.5324791
L_u[5,4] 0.5282536
L_u[5,5] 0.9744629
L_u[5,6] 0.0000000
L_u[5,7] 0.0000000
L_u[5,8] 0.0000000
L_u[5,9] 0.0000000
L_u[6,1] 0.6351133
L_u[6,2] 0.4436601
L_u[6,3] 0.5429731
L_u[6,4] 0.5391719
L_u[6,5] 0.5487482
L_u[6,6] 0.9428585
L_u[6,7] 0.0000000
L_u[6,8] 0.0000000
L_u[6,9] 0.0000000
L_u[7,1] 0.5562617
L_u[7,2] 0.5348565
L_u[7,3] 0.5514739
L_u[7,4] 0.5446926
L_u[7,5] 0.5476183
L_u[7,6] 0.5097937
L_u[7,7] 0.9153556
L_u[7,8] 0.0000000
L_u[7,9] 0.0000000
L_u[8,1] 0.4944285
L_u[8,2] 0.6159061
L_u[8,3] 0.5535389
L_u[8,4] 0.5561478
L_u[8,5] 0.5909508
L_u[8,6] 0.4867742
L_u[8,7] 0.5516640
L_u[8,8] 0.8887552
L_u[8,9] 0.0000000
L_u[9,1] 0.6442499
L_u[9,2] 0.5298522
L_u[9,3] 0.5422653
L_u[9,4] 0.5446919
L_u[9,5] 0.5491093
L_u[9,6] 0.5715091
L_u[9,7] 0.5583302
L_u[9,8] 0.5275687
L_u[9,9] 0.8357232
L_w[1,1] 1.0000000
L_w[1,2] 0.0000000
L_w[1,3] 0.0000000
L_w[2,1] 0.4571604
L_w[2,2] 0.9998347
L_w[2,3] 0.0000000
L_w[3,1] 0.6215726
L_w[3,2] 0.7886976
L_w[3,3] 0.9956475
z_u[1,1] -0.0535598
z_u[1,2] 1.3277736
z_u[1,3] 1.8427887
z_u[1,4] 1.7374492
z_u[1,5] 1.9529460
z_u[1,6] 1.9636886
z_u[1,7] 1.8837358
z_u[1,8] 1.7373880
z_u[1,9] 1.8479007
z_u[2,1] -0.2787172
z_u[2,2] 1.9232673
z_u[2,3] 1.8763920
z_u[2,4] 1.7435384
z_u[2,5] 1.8946007
z_u[2,6] 1.6691390
z_u[2,7] 1.9405676
z_u[2,8] 1.7928960
z_u[2,9] 2.0283817
z_u[3,1] 0.8041124
z_u[3,2] 1.3496413
z_u[3,3] 2.1776256
z_u[3,4] 1.9222296
z_u[3,5] 1.7465426
z_u[3,6] 1.6077870
z_u[3,7] 2.1181724
z_u[3,8] 1.8141622
z_u[3,9] 1.8201266
z_u[4,1] 1.5651933
z_u[4,2] 2.1107694
z_u[4,3] 1.6024478
[ reached getOption("max.print") -- omitted 2740 row(s) and 3 matrix slice(s) ]
# check params
summary(m1_gd, pars = c('beta[1]', 'beta[2]', 'beta[3]', 'beta[4]', 'beta[5]', 'beta[6]', 'beta[7]', 'beta[8]', 'beta[9]'))
$summary
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
beta[1] 5.713728 0.0011603 0.04449 5.62554 5.684340 5.713521 5.743626 5.79960 1470 1.0003
beta[2] 0.127819 0.0002814 0.02518 0.07672 0.111494 0.127758 0.144876 0.17621 8008 0.9996
beta[3] -0.011082 0.0002263 0.02377 -0.05825 -0.026802 -0.011387 0.004651 0.03581 11033 1.0001
beta[4] -0.051262 0.0006498 0.03962 -0.13019 -0.077218 -0.051252 -0.025040 0.02663 3717 1.0013
beta[5] -0.110649 0.0006325 0.04011 -0.18656 -0.138336 -0.111422 -0.083633 -0.03029 4021 1.0009
beta[6] 0.011734 0.0002219 0.02214 -0.03127 -0.003099 0.011363 0.026734 0.05562 9957 0.9996
beta[7] -0.024834 0.0002134 0.02185 -0.06698 -0.039378 -0.024796 -0.010466 0.01884 10482 1.0000
beta[8] -0.004565 0.0003525 0.03875 -0.08067 -0.030323 -0.004716 0.021033 0.07265 12087 0.9997
beta[9] -0.020618 0.0003378 0.03779 -0.09544 -0.045598 -0.020266 0.004869 0.05420 12517 0.9997
$c_summary
, , chains = chain:1
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.713138 0.04377 5.62740 5.683003 5.71254 5.743101 5.80120
beta[2] 0.127919 0.02495 0.07499 0.112037 0.12820 0.145146 0.17595
beta[3] -0.011122 0.02318 -0.05677 -0.026669 -0.01110 0.003678 0.03486
beta[4] -0.049603 0.03912 -0.12850 -0.075451 -0.04964 -0.023868 0.02721
beta[5] -0.112170 0.04045 -0.18641 -0.140518 -0.11349 -0.085299 -0.02756
beta[6] 0.011610 0.02235 -0.03314 -0.003383 0.01136 0.026635 0.05417
beta[7] -0.024474 0.02190 -0.06723 -0.039158 -0.02470 -0.009985 0.02005
beta[8] -0.004621 0.03793 -0.07997 -0.030144 -0.00500 0.020945 0.07093
beta[9] -0.020585 0.03690 -0.09351 -0.044811 -0.02013 0.003959 0.05356
, , chains = chain:2
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.715037 0.04484 5.62636 5.685916 5.715053 5.745564 5.80027
beta[2] 0.127851 0.02466 0.07754 0.112097 0.127127 0.144226 0.17510
beta[3] -0.010676 0.02410 -0.05826 -0.025909 -0.011212 0.004843 0.03648
beta[4] -0.051229 0.03913 -0.12902 -0.076160 -0.051931 -0.025282 0.02530
beta[5] -0.111027 0.03969 -0.18941 -0.138046 -0.111284 -0.083035 -0.03563
beta[6] 0.011793 0.02201 -0.03023 -0.003705 0.011121 0.026480 0.05697
beta[7] -0.024973 0.02246 -0.06628 -0.039961 -0.025135 -0.010662 0.02017
beta[8] -0.004396 0.03937 -0.08104 -0.030887 -0.003905 0.021598 0.07505
beta[9] -0.020592 0.03804 -0.09312 -0.046980 -0.020486 0.005699 0.05219
, , chains = chain:3
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.71471 0.04561 5.62498 5.686380 5.714693 5.745015 5.80040
beta[2] 0.12815 0.02577 0.07499 0.112058 0.128429 0.145906 0.17634
beta[3] -0.01096 0.02410 -0.05808 -0.027102 -0.011103 0.005638 0.03571
beta[4] -0.05028 0.04059 -0.12881 -0.077789 -0.050275 -0.022809 0.02851
beta[5] -0.11035 0.03992 -0.18650 -0.136889 -0.110758 -0.084419 -0.02947
beta[6] 0.01177 0.02219 -0.03032 -0.003241 0.011586 0.026975 0.05532
beta[7] -0.02511 0.02162 -0.06740 -0.039077 -0.024977 -0.010970 0.01809
beta[8] -0.00539 0.03962 -0.08210 -0.032134 -0.006221 0.021030 0.07272
beta[9] -0.02108 0.03804 -0.09897 -0.046205 -0.020819 0.004461 0.05202
, , chains = chain:4
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.712026 0.04368 5.62513 5.682875 5.712404 5.740672 5.79525
beta[2] 0.127359 0.02535 0.07959 0.109347 0.127041 0.143869 0.17762
beta[3] -0.011570 0.02368 -0.05916 -0.027653 -0.011936 0.004601 0.03554
beta[4] -0.053935 0.03951 -0.13240 -0.080639 -0.053332 -0.027947 0.02386
beta[5] -0.109051 0.04033 -0.18296 -0.137600 -0.109879 -0.081789 -0.02636
beta[6] 0.011759 0.02205 -0.03076 -0.002476 0.011476 0.026602 0.05549
beta[7] -0.024785 0.02142 -0.06650 -0.039542 -0.024316 -0.010204 0.01554
beta[8] -0.003853 0.03808 -0.07760 -0.028500 -0.004147 0.020882 0.07188
beta[9] -0.020212 0.03820 -0.09636 -0.044417 -0.019729 0.005767 0.05676
# check convergence
traceplot(m1_gd, pars = c("beta"))
# check predicts --> posterior parameter distr.
y_posterior <- extract(m1_gd)
y_posterior$beta[,1] #intercept
[1] 5.751 5.751 5.679 5.755 5.769 5.735 5.704 5.784 5.787 5.658 5.768 5.707 5.673 5.663 5.671 5.740
[17] 5.708 5.772 5.714 5.732 5.699 5.704 5.769 5.763 5.655 5.689 5.732 5.714 5.739 5.706 5.723 5.655
[33] 5.773 5.697 5.695 5.676 5.726 5.752 5.669 5.665 5.715 5.734 5.594 5.739 5.707 5.711 5.747 5.695
[49] 5.740 5.748 5.720 5.726 5.722 5.749 5.761 5.679 5.722 5.667 5.724 5.655 5.741 5.705 5.757 5.717
[65] 5.704 5.630 5.604 5.754 5.710 5.710 5.645 5.677 5.760 5.705 5.679 5.775 5.707 5.771 5.724 5.699
[81] 5.762 5.743 5.684 5.704 5.631 5.763 5.689 5.667 5.705 5.743 5.741 5.731 5.736 5.700 5.652 5.788
[97] 5.747 5.753 5.703 5.705 5.803 5.691 5.667 5.658 5.699 5.650 5.733 5.702 5.685 5.748 5.788 5.733
[113] 5.671 5.716 5.665 5.724 5.709 5.694 5.739 5.736 5.752 5.745 5.644 5.749 5.691 5.700 5.637 5.672
[129] 5.735 5.697 5.701 5.713 5.712 5.759 5.709 5.651 5.697 5.757 5.691 5.743 5.705 5.731 5.699 5.659
[145] 5.714 5.696 5.687 5.697 5.705 5.783 5.723 5.702 5.732 5.748 5.692 5.734 5.756 5.803 5.702 5.774
[161] 5.805 5.757 5.728 5.696 5.702 5.677 5.678 5.686 5.676 5.700 5.681 5.716 5.703 5.714 5.737 5.685
[177] 5.677 5.697 5.739 5.731 5.734 5.808 5.703 5.735 5.809 5.677 5.748 5.722 5.651 5.803 5.726 5.656
[193] 5.668 5.727 5.767 5.820 5.742 5.733 5.624 5.719 5.668 5.663 5.769 5.761 5.768 5.790 5.709 5.692
[209] 5.684 5.789 5.675 5.734 5.668 5.698 5.721 5.686 5.793 5.657 5.696 5.739 5.655 5.740 5.690 5.656
[225] 5.727 5.694 5.718 5.704 5.694 5.698 5.722 5.733 5.665 5.726 5.742 5.704 5.684 5.681 5.736 5.643
[241] 5.702 5.752 5.693 5.717 5.687 5.750 5.740 5.750 5.760 5.651 5.747 5.721 5.739 5.757 5.755 5.676
[257] 5.730 5.767 5.654 5.786 5.701 5.735 5.664 5.691 5.742 5.741 5.699 5.715 5.673 5.711 5.714 5.787
[273] 5.807 5.709 5.694 5.842 5.723 5.741 5.762 5.648 5.668 5.674 5.685 5.701 5.653 5.717 5.613 5.751
[289] 5.754 5.691 5.694 5.757 5.763 5.621 5.683 5.728 5.643 5.679 5.681 5.719 5.668 5.743 5.723 5.684
[305] 5.711 5.777 5.755 5.723 5.735 5.721 5.724 5.656 5.683 5.646 5.798 5.765 5.776 5.683 5.778 5.698
[321] 5.697 5.607 5.779 5.649 5.703 5.665 5.632 5.686 5.782 5.703 5.715 5.686 5.715 5.716 5.715 5.738
[337] 5.719 5.680 5.717 5.675 5.699 5.776 5.718 5.745 5.584 5.757 5.706 5.716 5.701 5.691 5.679 5.742
[353] 5.662 5.680 5.717 5.769 5.698 5.750 5.701 5.740 5.835 5.702 5.717 5.730 5.754 5.651 5.740 5.685
[369] 5.698 5.673 5.709 5.675 5.798 5.700 5.682 5.671 5.628 5.734 5.642 5.714 5.754 5.661 5.700 5.754
[385] 5.726 5.818 5.761 5.733 5.696 5.712 5.654 5.701 5.782 5.668 5.689 5.734 5.737 5.750 5.796 5.695
[401] 5.775 5.664 5.753 5.761 5.703 5.711 5.747 5.696 5.738 5.644 5.676 5.689 5.727 5.719 5.769 5.716
[417] 5.719 5.683 5.664 5.727 5.671 5.695 5.758 5.736 5.754 5.738 5.704 5.629 5.729 5.677 5.677 5.691
[433] 5.696 5.781 5.652 5.670 5.646 5.724 5.688 5.685 5.742 5.755 5.734 5.790 5.688 5.735 5.706 5.720
[449] 5.751 5.629 5.732 5.655 5.798 5.718 5.660 5.735 5.753 5.739 5.708 5.710 5.746 5.672 5.703 5.700
[465] 5.728 5.677 5.691 5.690 5.792 5.754 5.733 5.676 5.698 5.791 5.704 5.729 5.696 5.640 5.655 5.759
[481] 5.676 5.684 5.670 5.719 5.699 5.712 5.678 5.649 5.715 5.677 5.733 5.692 5.664 5.755 5.664 5.624
[497] 5.738 5.682 5.678 5.624 5.708 5.716 5.687 5.719 5.732 5.697 5.618 5.740 5.770 5.670 5.699 5.716
[513] 5.699 5.743 5.741 5.745 5.757 5.671 5.721 5.709 5.737 5.649 5.777 5.665 5.687 5.709 5.688 5.709
[529] 5.720 5.783 5.791 5.735 5.709 5.606 5.780 5.724 5.672 5.738 5.668 5.676 5.695 5.692 5.714 5.851
[545] 5.772 5.764 5.686 5.673 5.753 5.678 5.752 5.727 5.714 5.683 5.635 5.727 5.673 5.713 5.786 5.681
[561] 5.618 5.773 5.726 5.705 5.743 5.740 5.644 5.744 5.706 5.704 5.743 5.676 5.735 5.792 5.766 5.746
[577] 5.754 5.616 5.759 5.680 5.712 5.667 5.681 5.681 5.782 5.725 5.698 5.709 5.696 5.762 5.713 5.692
[593] 5.675 5.717 5.709 5.690 5.673 5.694 5.693 5.652 5.685 5.708 5.704 5.696 5.686 5.747 5.678 5.687
[609] 5.737 5.700 5.752 5.715 5.756 5.650 5.767 5.747 5.707 5.747 5.712 5.690 5.719 5.731 5.671 5.708
[625] 5.695 5.669 5.784 5.675 5.715 5.729 5.657 5.647 5.720 5.735 5.708 5.707 5.681 5.734 5.644 5.695
[641] 5.673 5.708 5.685 5.722 5.701 5.671 5.759 5.688 5.688 5.698 5.766 5.801 5.710 5.701 5.775 5.716
[657] 5.710 5.738 5.727 5.726 5.652 5.737 5.789 5.734 5.713 5.691 5.753 5.771 5.627 5.575 5.724 5.733
[673] 5.697 5.707 5.762 5.720 5.696 5.656 5.769 5.728 5.689 5.765 5.690 5.762 5.694 5.673 5.691 5.706
[689] 5.676 5.774 5.711 5.722 5.693 5.731 5.799 5.654 5.702 5.681 5.744 5.657 5.727 5.688 5.759 5.762
[705] 5.753 5.718 5.816 5.624 5.739 5.647 5.682 5.729 5.713 5.723 5.673 5.770 5.618 5.725 5.746 5.717
[721] 5.671 5.693 5.636 5.608 5.719 5.700 5.740 5.737 5.699 5.749 5.668 5.724 5.755 5.816 5.736 5.754
[737] 5.712 5.721 5.715 5.667 5.757 5.704 5.718 5.738 5.778 5.693 5.772 5.668 5.721 5.703 5.708 5.675
[753] 5.676 5.733 5.752 5.627 5.677 5.706 5.741 5.715 5.754 5.719 5.745 5.666 5.674 5.699 5.670 5.673
[769] 5.740 5.689 5.661 5.709 5.681 5.715 5.730 5.675 5.713 5.770 5.727 5.650 5.722 5.720 5.757 5.710
[785] 5.718 5.732 5.701 5.717 5.768 5.649 5.703 5.683 5.706 5.743 5.692 5.675 5.690 5.694 5.725 5.694
[801] 5.710 5.731 5.778 5.720 5.781 5.727 5.709 5.732 5.694 5.740 5.658 5.744 5.745 5.738 5.820 5.699
[817] 5.697 5.676 5.670 5.749 5.701 5.777 5.749 5.731 5.604 5.623 5.731 5.743 5.688 5.659 5.710 5.760
[833] 5.758 5.765 5.713 5.711 5.636 5.731 5.652 5.631 5.664 5.640 5.756 5.658 5.763 5.627 5.637 5.711
[849] 5.669 5.690 5.746 5.728 5.711 5.738 5.702 5.707 5.762 5.746 5.717 5.634 5.770 5.627 5.720 5.649
[865] 5.748 5.770 5.760 5.765 5.760 5.799 5.688 5.677 5.757 5.747 5.687 5.647 5.731 5.619 5.645 5.758
[881] 5.688 5.728 5.712 5.699 5.676 5.769 5.687 5.745 5.660 5.758 5.737 5.660 5.709 5.698 5.737 5.678
[897] 5.785 5.707 5.791 5.746 5.683 5.663 5.648 5.708 5.723 5.675 5.704 5.709 5.701 5.677 5.652 5.705
[913] 5.753 5.768 5.703 5.705 5.707 5.832 5.716 5.692 5.727 5.713 5.809 5.682 5.692 5.705 5.699 5.682
[929] 5.701 5.624 5.671 5.684 5.799 5.660 5.656 5.656 5.788 5.819 5.693 5.758 5.724 5.723 5.714 5.637
[945] 5.755 5.716 5.652 5.770 5.752 5.683 5.708 5.695 5.710 5.722 5.692 5.690 5.719 5.711 5.766 5.749
[961] 5.779 5.692 5.726 5.731 5.746 5.694 5.766 5.753 5.675 5.764 5.640 5.672 5.659 5.658 5.777 5.690
[977] 5.672 5.740 5.713 5.749 5.698 5.724 5.753 5.718 5.680 5.768 5.704 5.662 5.681 5.752 5.730 5.758
[993] 5.661 5.747 5.744 5.603 5.741 5.778 5.674 5.789
[ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,2] #Gram
[1] 0.08617 0.14450 0.14781 0.12892 0.13129 0.13293 0.13507 0.14327 0.14966 0.14901 0.15392 0.14168
[13] 0.14565 0.12623 0.15916 0.10220 0.11038 0.14037 0.13081 0.14557 0.14672 0.13640 0.09500 0.17139
[25] 0.16874 0.15347 0.14150 0.14487 0.13399 0.16667 0.12961 0.09932 0.10611 0.11766 0.13663 0.13142
[37] 0.13723 0.07287 0.12577 0.06899 0.08543 0.12287 0.14221 0.14713 0.14097 0.07892 0.15540 0.11159
[49] 0.11691 0.10813 0.11586 0.14688 0.13697 0.13043 0.15569 0.14091 0.07121 0.12177 0.13269 0.10608
[61] 0.14402 0.12714 0.14093 0.14937 0.11413 0.12640 0.09119 0.13837 0.11502 0.10160 0.11414 0.07872
[73] 0.12658 0.12101 0.12752 0.11559 0.15402 0.14726 0.11283 0.13488 0.13121 0.18106 0.11671 0.15492
[85] 0.13950 0.12340 0.08882 0.13593 0.16429 0.14245 0.13861 0.12153 0.09980 0.10797 0.09945 0.12834
[97] 0.15143 0.12603 0.07650 0.13247 0.09456 0.15212 0.13793 0.13436 0.09781 0.15148 0.16614 0.11385
[109] 0.13221 0.15689 0.09666 0.11132 0.15233 0.10938 0.09012 0.09036 0.10308 0.14140 0.09856 0.13506
[121] 0.11320 0.11403 0.15441 0.10683 0.11736 0.14178 0.14920 0.12592 0.12665 0.11999 0.16340 0.15276
[133] 0.11877 0.12594 0.15326 0.13985 0.16150 0.13362 0.08675 0.13806 0.11504 0.15282 0.08628 0.12197
[145] 0.12630 0.12706 0.09020 0.13931 0.11416 0.09610 0.11782 0.13046 0.12236 0.10923 0.12507 0.10658
[157] 0.10899 0.10802 0.14134 0.12987 0.12139 0.13758 0.16275 0.14353 0.12167 0.14724 0.10874 0.10913
[169] 0.12759 0.11972 0.12935 0.09372 0.10615 0.11235 0.14458 0.10970 0.07592 0.10678 0.15926 0.13222
[181] 0.09524 0.13321 0.14408 0.17678 0.13372 0.13564 0.17511 0.16764 0.12833 0.13570 0.10194 0.08344
[193] 0.14541 0.05377 0.15002 0.14793 0.12174 0.15278 0.09622 0.12525 0.13503 0.11550 0.14684 0.15213
[205] 0.10659 0.11622 0.11635 0.11295 0.16448 0.10423 0.12234 0.12843 0.14249 0.10947 0.14683 0.11571
[217] 0.13161 0.13919 0.12992 0.13853 0.14603 0.13049 0.07034 0.10650 0.08632 0.14786 0.12386 0.17493
[229] 0.15394 0.10624 0.14887 0.10067 0.12072 0.11424 0.15735 0.16042 0.13650 0.16613 0.17444 0.12783
[241] 0.14970 0.14304 0.11044 0.15157 0.15409 0.12360 0.11551 0.14503 0.12144 0.15400 0.12469 0.14487
[253] 0.13745 0.12516 0.15474 0.13063 0.13175 0.12857 0.12539 0.11606 0.12027 0.19170 0.14063 0.08966
[265] 0.15662 0.18473 0.08527 0.15371 0.09278 0.13630 0.10137 0.16206 0.11499 0.10935 0.11129 0.13247
[277] 0.09457 0.10138 0.12166 0.12270 0.14339 0.12127 0.11183 0.10896 0.13236 0.10664 0.15681 0.13962
[289] 0.13195 0.11184 0.09870 0.13971 0.07197 0.16672 0.12538 0.16240 0.10174 0.09411 0.09811 0.13354
[301] 0.12356 0.13345 0.13153 0.11092 0.14653 0.11313 0.11546 0.13909 0.09056 0.12839 0.12640 0.09912
[313] 0.15115 0.19916 0.14901 0.11999 0.11155 0.11490 0.13676 0.09680 0.12861 0.07439 0.10428 0.13824
[325] 0.14970 0.10536 0.14174 0.10326 0.12899 0.14187 0.14581 0.15519 0.11016 0.09563 0.10252 0.13397
[337] 0.16051 0.12326 0.11764 0.14207 0.12551 0.11481 0.15527 0.10294 0.06382 0.15177 0.11655 0.14535
[349] 0.11328 0.14300 0.14893 0.14658 0.14335 0.12075 0.11869 0.09880 0.13223 0.15161 0.12589 0.19733
[361] 0.12428 0.13698 0.13464 0.14514 0.10326 0.10642 0.14590 0.15283 0.15162 0.10011 0.12288 0.16207
[373] 0.11674 0.13134 0.09859 0.13174 0.10200 0.10511 0.11659 0.12234 0.15189 0.12752 0.12517 0.12137
[385] 0.12267 0.04446 0.11596 0.13643 0.15836 0.14658 0.10246 0.16870 0.18755 0.16820 0.10961 0.18075
[397] 0.11354 0.07171 0.09722 0.17022 0.11216 0.16280 0.15870 0.20582 0.14103 0.14484 0.17494 0.13771
[409] 0.12974 0.15814 0.14417 0.14738 0.14563 0.08168 0.11137 0.15177 0.12507 0.11288 0.16363 0.10223
[421] 0.14722 0.11968 0.15899 0.12449 0.13869 0.09205 0.13671 0.15691 0.11568 0.14063 0.12028 0.12888
[433] 0.11979 0.06626 0.14543 0.08626 0.09647 0.07153 0.13870 0.13193 0.14451 0.14357 0.08044 0.16551
[445] 0.13039 0.15365 0.08732 0.10992 0.12014 0.11644 0.14069 0.12645 0.14660 0.09043 0.09341 0.14937
[457] 0.11532 0.14833 0.09702 0.12638 0.12350 0.11720 0.14513 0.11679 0.11915 0.12738 0.09644 0.13432
[469] 0.10587 0.13283 0.10026 0.17041 0.11957 0.13881 0.12078 0.12541 0.11916 0.16420 0.09504 0.15381
[481] 0.15325 0.11999 0.16943 0.08005 0.13699 0.08099 0.13111 0.06213 0.12644 0.10711 0.10377 0.16842
[493] 0.10034 0.14969 0.12705 0.14769 0.15639 0.12379 0.10424 0.08496 0.13809 0.18324 0.13765 0.13690
[505] 0.08981 0.09890 0.11781 0.11738 0.10540 0.09849 0.17603 0.13824 0.14696 0.18071 0.14536 0.12971
[517] 0.12861 0.11966 0.11337 0.14504 0.09835 0.15846 0.09273 0.14971 0.17260 0.08591 0.13329 0.14821
[529] 0.18201 0.15028 0.11969 0.17040 0.13395 0.13105 0.13440 0.15633 0.13406 0.04336 0.13434 0.12383
[541] 0.14595 0.15120 0.13406 0.12190 0.10521 0.11391 0.14633 0.17315 0.14189 0.10951 0.16390 0.20014
[553] 0.15735 0.12947 0.14806 0.11584 0.10904 0.11258 0.14033 0.17200 0.11461 0.13188 0.11179 0.09028
[565] 0.13988 0.11073 0.12052 0.15197 0.11854 0.14750 0.13312 0.16275 0.14386 0.13822 0.12464 0.12385
[577] 0.14302 0.10609 0.15688 0.11489 0.10273 0.11644 0.07321 0.13547 0.12621 0.13460 0.18187 0.15457
[589] 0.08827 0.14831 0.13677 0.15855 0.13647 0.06721 0.10787 0.13113 0.12921 0.13534 0.11620 0.09270
[601] 0.09484 0.09778 0.12848 0.14979 0.16408 0.11892 0.14428 0.12656 0.11207 0.10928 0.14767 0.08120
[613] 0.12933 0.08151 0.13366 0.13624 0.18558 0.14420 0.14177 0.12611 0.06975 0.15571 0.13356 0.12182
[625] 0.09578 0.11959 0.15866 0.08269 0.14066 0.10946 0.17298 0.10976 0.11781 0.13868 0.05977 0.13679
[637] 0.12436 0.07208 0.13179 0.08501 0.11879 0.11218 0.13005 0.14813 0.11696 0.13584 0.11975 0.06660
[649] 0.16091 0.11411 0.12108 0.13884 0.11474 0.12107 0.17435 0.12664 0.18112 0.13733 0.12864 0.16557
[661] 0.09005 0.10089 0.11636 0.11584 0.12421 0.12055 0.13789 0.14543 0.10242 0.07911 0.09262 0.12197
[673] 0.16440 0.14262 0.12237 0.11486 0.12937 0.14811 0.14572 0.14774 0.14459 0.12204 0.11245 0.14830
[685] 0.09418 0.12415 0.12969 0.03976 0.13366 0.09318 0.18340 0.13630 0.13642 0.13825 0.12225 0.13120
[697] 0.06837 0.11119 0.13583 0.15370 0.11017 0.13900 0.14234 0.12275 0.13802 0.12296 0.14886 0.13570
[709] 0.11289 0.10159 0.16925 0.11048 0.12514 0.09460 0.14195 0.11614 0.13376 0.11309 0.08897 0.14748
[721] 0.12434 0.12826 0.10744 0.15766 0.10934 0.14036 0.16213 0.08833 0.08198 0.18223 0.11762 0.13460
[733] 0.14590 0.12241 0.09997 0.13471 0.15441 0.15375 0.12639 0.14780 0.12179 0.16359 0.09880 0.15218
[745] 0.15039 0.14517 0.14172 0.17587 0.12947 0.13115 0.12604 0.10719 0.12735 0.12851 0.11255 0.12703
[757] 0.13626 0.10706 0.13023 0.11124 0.15471 0.12989 0.11844 0.11613 0.12551 0.13302 0.16780 0.13311
[769] 0.11801 0.12862 0.13812 0.12966 0.12752 0.14380 0.10379 0.09667 0.12599 0.13431 0.12687 0.11303
[781] 0.12263 0.09326 0.10503 0.14186 0.13954 0.14169 0.14216 0.19075 0.12190 0.13115 0.11137 0.10626
[793] 0.07689 0.07665 0.14223 0.11591 0.14908 0.13715 0.10665 0.15157 0.10092 0.16448 0.14414 0.10579
[805] 0.14298 0.13891 0.13119 0.12187 0.16606 0.12191 0.13380 0.15242 0.14120 0.15015 0.12004 0.07321
[817] 0.14435 0.18583 0.09775 0.13144 0.10317 0.10751 0.10747 0.12284 0.09342 0.11387 0.10395 0.13351
[829] 0.18951 0.11376 0.10294 0.12265 0.15853 0.13155 0.14616 0.15013 0.09802 0.12476 0.14579 0.10173
[841] 0.09113 0.10351 0.12155 0.11525 0.11997 0.12907 0.13051 0.16481 0.14488 0.10562 0.13479 0.13406
[853] 0.11385 0.14188 0.16336 0.09901 0.11036 0.13098 0.14489 0.11368 0.10775 0.10988 0.17271 0.14885
[865] 0.14082 0.11923 0.13267 0.11739 0.14694 0.12680 0.15773 0.08823 0.14693 0.14772 0.10690 0.11177
[877] 0.14767 0.12014 0.08837 0.14243 0.12652 0.13006 0.11516 0.14220 0.12479 0.12474 0.13651 0.17067
[889] 0.10511 0.10579 0.14242 0.07305 0.15001 0.17609 0.13973 0.11461 0.14059 0.09798 0.15346 0.11536
[901] 0.10475 0.10956 0.15445 0.13200 0.10959 0.13826 0.11600 0.07504 0.13317 0.14467 0.15274 0.07781
[913] 0.12626 0.13610 0.16437 0.11658 0.12503 0.11983 0.11008 0.11812 0.16014 0.11373 0.15979 0.16546
[925] 0.15146 0.09879 0.07878 0.16929 0.07895 0.13191 0.14494 0.15776 0.13905 0.11267 0.10646 0.14932
[937] 0.15015 0.15001 0.06972 0.16959 0.09255 0.09434 0.10668 0.14737 0.14571 0.13768 0.10172 0.13162
[949] 0.12990 0.12523 0.14373 0.15639 0.14883 0.10322 0.10213 0.14080 0.13515 0.13795 0.16567 0.13562
[961] 0.14240 0.11704 0.13071 0.10481 0.13580 0.08970 0.14595 0.12253 0.12586 0.16377 0.14675 0.15883
[973] 0.10412 0.10696 0.07533 0.13344 0.06805 0.15634 0.11425 0.12240 0.13767 0.10120 0.14719 0.12186
[985] 0.12799 0.16315 0.06677 0.11491 0.14141 0.17970 0.07001 0.14943 0.11737 0.16799 0.08687 0.06966
[997] 0.14371 0.16850 0.13180 0.13108
[ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,3] #Gen
[1] -0.00106944 0.01723637 -0.00707412 -0.01223233 -0.00727736 0.03102150 0.00780335 0.01196708
[9] 0.00060111 -0.00344965 -0.03276547 0.01570662 -0.03691361 0.01670390 0.00211250 -0.04958785
[17] 0.02476726 -0.01359971 0.01159031 0.02364573 0.03294483 0.03331003 -0.00368468 -0.01160258
[25] -0.03407884 -0.02242436 -0.01664567 -0.00280030 -0.03347919 -0.02060326 -0.05208339 -0.01961318
[33] -0.02815123 0.00331478 -0.01284463 -0.01367274 -0.02595851 -0.01838227 -0.00002843 -0.03618458
[41] -0.01911992 0.00769638 -0.02233802 -0.01631122 -0.03514461 -0.03382256 -0.04279913 -0.05639932
[49] -0.00841101 0.02908361 -0.01428011 -0.03452012 0.00285543 -0.04276985 0.02530807 0.02828814
[57] -0.01073406 -0.02072174 -0.01463113 -0.03408588 0.06491618 -0.04641248 0.00018524 0.01746849
[65] 0.02410551 -0.02554732 -0.03213040 -0.03202411 -0.03309063 -0.04789999 0.01868149 -0.01800614
[73] -0.07243857 -0.00036751 -0.00078828 0.01255899 0.01610201 0.02360909 -0.03459102 0.00979454
[81] -0.03137738 -0.00196057 -0.03465186 -0.03683959 -0.00795662 -0.03537149 -0.01463421 -0.02080247
[89] -0.02633286 0.01936775 -0.01110323 -0.03832089 -0.02237906 -0.02946058 -0.04860224 -0.00710820
[97] -0.02651200 -0.01536221 -0.01209059 -0.01820591 -0.02934318 0.02189592 -0.03323197 0.01908551
[105] 0.00734194 -0.00799459 0.00371379 -0.03003360 -0.03243708 -0.00525939 -0.03220836 -0.00280695
[113] 0.01631044 -0.01961994 -0.03633019 -0.02685439 -0.01763903 -0.00592809 -0.00707304 -0.04679316
[121] 0.00601756 -0.01155093 -0.00244633 -0.00423372 0.00627744 -0.00284373 0.01098382 -0.01582925
[129] -0.01773085 0.01162967 0.01780629 0.01568926 -0.01706149 0.01936326 -0.01371813 0.01019764
[137] -0.04970900 0.01175227 -0.00364758 0.00517922 -0.00252040 -0.02988482 -0.03316348 0.00363462
[145] -0.00724080 -0.01502081 -0.00130272 0.00984024 -0.02423766 -0.02989626 -0.05469784 0.01299272
[153] -0.00126771 -0.03893603 0.03084896 0.00541898 0.00169659 -0.02932019 -0.02668506 -0.02836923
[161] -0.01196805 0.01924242 -0.01967250 0.00981299 0.00329113 -0.01166762 -0.03326474 -0.00786504
[169] -0.01477161 -0.00342725 0.00522616 -0.04724767 0.02407782 0.00808069 -0.02637878 -0.03037316
[177] -0.05359858 -0.02430850 -0.02586993 -0.03564347 -0.01689138 -0.01264732 -0.03084462 0.02797533
[185] -0.04126085 -0.01275005 -0.01103491 0.01012007 -0.00796038 0.03335229 0.00607155 0.03412742
[193] 0.01020440 0.00005119 0.03512643 -0.01002376 -0.02842485 -0.04169525 -0.02244722 -0.00388589
[201] 0.00342806 0.00608732 -0.03242061 0.00029119 -0.01230875 0.05841400 0.00844215 -0.01079805
[209] -0.03913770 -0.02528725 0.02791950 0.00358349 -0.00021585 0.02551071 -0.00942989 0.00568125
[217] -0.02119962 0.00631696 0.02434835 0.02028426 -0.05079196 -0.02489043 -0.02409695 -0.02655619
[225] -0.02232372 -0.02316131 -0.02351124 -0.00619559 -0.01888899 -0.03413848 -0.01931235 0.01975430
[233] -0.02900788 -0.04202131 0.02240931 -0.02088645 -0.01760849 -0.05567408 -0.02926210 -0.06655290
[241] -0.02245812 -0.03541059 -0.01808600 0.01946438 0.04481387 0.01558987 0.00997865 -0.00233513
[249] -0.04220607 -0.00127102 -0.01164510 -0.02381192 0.04088900 -0.04206640 -0.01683782 -0.01657179
[257] 0.00162341 -0.03766728 -0.01725767 0.06457781 -0.00709269 0.00489113 -0.00845587 0.00847901
[265] -0.01197224 0.01932045 -0.01149348 0.03788221 0.01766121 -0.04309136 0.00028310 0.00003375
[273] -0.00919706 -0.05949156 -0.00291287 -0.00706582 0.01257272 -0.01914974 0.00177092 -0.00595267
[281] 0.02207284 0.00162421 -0.02896840 -0.06484883 0.03746989 -0.01045558 0.03116725 0.00982722
[289] -0.03014796 -0.00983561 -0.00353908 0.02101640 0.00202344 0.00901520 0.01665022 -0.03458736
[297] -0.02545730 -0.02842022 0.00386381 -0.04129583 -0.03258765 -0.03016905 0.00666764 -0.00822739
[305] 0.02375268 -0.01667201 -0.01582260 -0.02617007 -0.02621151 -0.03164478 -0.02141022 -0.01359232
[313] 0.01896960 -0.02062319 -0.00253318 0.01589958 -0.00132210 -0.00278092 -0.02283437 -0.01221491
[321] -0.04634593 0.01435119 -0.00540659 -0.03972040 -0.00445768 0.00778914 -0.01705931 -0.03095313
[329] -0.00925838 -0.00844864 -0.03830754 -0.03593573 -0.04070685 -0.02078757 -0.00508927 -0.02852232
[337] 0.00658868 -0.02314283 -0.01290900 -0.03449931 -0.03447128 -0.01584038 -0.01826927 -0.00872635
[345] -0.00259939 -0.02339660 0.00486272 -0.05098474 -0.01351135 0.00430683 -0.01654341 0.01790155
[353] 0.00649948 -0.03737773 0.01984770 0.02065052 0.00011394 -0.00545146 0.01981835 -0.02157864
[361] -0.01465082 -0.01822516 -0.05112824 0.00208944 0.03748515 0.00208152 -0.01956229 0.01233414
[369] -0.04405349 0.00366630 -0.01195947 -0.03133940 0.03794244 -0.03524457 -0.01327844 -0.04865635
[377] 0.02815125 -0.00660625 -0.00556524 -0.00046465 -0.02510306 -0.05670267 0.00698370 -0.01587887
[385] -0.01552686 -0.00953153 0.03599736 0.01085109 -0.01905755 -0.00754857 0.00903336 0.02597144
[393] -0.02077984 -0.02312637 0.00081366 -0.00888338 0.01781148 -0.01974338 0.00837198 0.02992075
[401] 0.00016919 -0.01777196 -0.01686568 -0.02472697 0.01683596 -0.00115752 -0.00028660 -0.02589050
[409] -0.03492567 -0.02522668 -0.00248249 -0.03172562 0.00920490 -0.04678724 -0.01257646 0.02196856
[417] 0.00690270 -0.03185368 -0.03081723 -0.02520064 0.00496535 -0.05521860 -0.00479881 -0.06056265
[425] -0.03258747 0.00099649 -0.05228077 -0.02108835 -0.00029120 -0.02813967 -0.04107349 -0.03174505
[433] -0.02113880 -0.01816748 0.03238059 0.00803342 0.03766730 0.01776451 -0.02582345 0.00791820
[441] -0.01096682 0.04238890 -0.00885886 -0.01110628 0.00027975 -0.03184796 0.01112439 -0.01299737
[449] -0.03296547 0.01137316 -0.01462192 0.00325612 -0.01079400 -0.00676842 -0.01052409 0.03477417
[457] 0.00921222 0.01885366 -0.01468075 -0.01808948 -0.02051672 -0.03028779 -0.01178273 -0.02387978
[465] -0.02497720 0.01635957 -0.01762283 -0.05129534 -0.01551434 -0.04395930 -0.00972292 -0.03370353
[473] -0.04263258 -0.05017151 -0.01715730 -0.05990051 -0.04053610 -0.01753843 -0.01544986 0.00373972
[481] -0.05302933 -0.03952766 -0.01989581 -0.01054773 -0.03492909 0.00210697 -0.00374140 -0.02833263
[489] 0.01370584 -0.04438683 -0.03012874 0.00233946 -0.03668764 0.01997901 0.00902279 0.02087751
[497] -0.01892860 -0.01628297 -0.03802780 0.00176971 0.00210700 -0.06014376 -0.02507292 -0.00727020
[505] -0.03372086 0.02196662 -0.00241072 0.00996763 -0.03282617 -0.01049957 -0.00560291 -0.03464997
[513] -0.04086157 -0.01268797 -0.01035984 -0.03935688 -0.01542797 0.04062241 -0.01391082 -0.02681293
[521] 0.00100328 -0.00631745 -0.01628477 -0.00546902 0.03568360 0.02363068 -0.02324745 -0.02622676
[529] -0.01720064 0.01428435 0.01227005 -0.01677244 -0.03239156 -0.00693315 0.00098400 0.00572660
[537] -0.01183543 0.01044649 -0.02488978 0.01333316 -0.03442890 -0.00438871 -0.00700452 -0.02462506
[545] 0.01231491 0.03157708 -0.04024764 -0.00683572 0.00703481 -0.03982794 0.00301000 0.00773979
[553] -0.00683832 -0.05775127 0.01513957 -0.02035579 -0.01687040 -0.03722332 -0.03409375 -0.03492758
[561] -0.03924802 -0.03758798 -0.00606526 -0.02602327 0.00507645 -0.02023009 -0.01311032 -0.00857505
[569] -0.01567366 -0.03882807 0.01846517 -0.03991775 -0.01024226 -0.02290371 -0.01998916 -0.01877028
[577] -0.03044583 0.04748663 0.03546417 -0.00663156 0.00880826 -0.00973563 -0.01853473 -0.00619357
[585] 0.00461061 0.04822562 -0.02162730 0.01028704 0.00990726 0.00205172 -0.03544957 -0.02711232
[593] -0.05796819 -0.03307294 -0.05878819 -0.00919060 -0.01122971 0.00632304 0.00718582 -0.00067684
[601] -0.02942251 -0.01564453 -0.02172695 -0.02362901 0.00516568 -0.01394422 -0.00242754 -0.02801939
[609] -0.00309194 -0.02384221 -0.00436496 0.00883081 -0.00026676 -0.00315559 -0.00132654 0.01611153
[617] -0.00832194 -0.02211942 -0.03257068 0.05218372 0.00183719 -0.01876673 -0.01588151 -0.00646693
[625] -0.01427250 -0.03213229 0.00890484 -0.02481306 -0.00929065 -0.05987277 0.02428045 0.01711637
[633] 0.00730879 -0.00892640 -0.00738414 -0.02997289 0.00979930 -0.00190055 0.00239534 0.00084795
[641] -0.02193393 0.00099078 -0.01187751 -0.03648457 -0.05557616 0.00076893 -0.02145352 -0.02912481
[649] 0.01433732 -0.00123894 -0.00004007 0.01225740 0.01117872 -0.00811510 -0.01604459 0.07323063
[657] -0.04747832 -0.02410094 -0.02893800 -0.00701050 0.01995386 0.02005784 0.01314236 -0.02637196
[665] -0.01619821 -0.02758845 -0.03565105 -0.02334607 0.00242444 0.00265449 -0.02034734 0.01083825
[673] -0.02258150 -0.00504424 0.03422268 0.01223938 -0.00735531 0.04015122 -0.01997087 -0.02554533
[681] 0.04312141 0.01520341 -0.04792968 0.05938690 0.01557169 0.00688373 -0.01566435 -0.00958298
[689] -0.01041508 -0.00472088 -0.04064655 -0.00105047 0.00832744 0.00081818 0.02160404 -0.02580389
[697] -0.00901546 0.00011406 0.00067579 -0.00326265 -0.01492123 -0.03929750 -0.01969733 0.04071537
[705] -0.01357449 0.01363671 -0.02494684 -0.06536937 -0.00732026 -0.03834445 0.00863856 -0.02242512
[713] -0.01224648 -0.03390430 -0.01496557 -0.02922993 -0.01208147 0.02468910 -0.05863531 -0.01225846
[721] -0.02029693 -0.02045500 -0.02450003 0.02054137 0.01162721 -0.05112858 -0.00229661 0.01457119
[729] -0.05279079 -0.01699768 -0.01982464 0.02441704 0.01849751 -0.04370533 -0.03389876 0.00291913
[737] -0.01709731 -0.04704729 -0.01487041 -0.01589592 -0.01093381 0.00743658 -0.05033107 0.01113807
[745] 0.01199589 -0.00869008 -0.03187038 -0.01490141 0.00431069 -0.00836946 -0.01933009 -0.01299569
[753] 0.01939044 -0.01211408 -0.03175936 0.00104045 -0.06231909 -0.00704012 0.00121711 -0.03546202
[761] -0.00837727 0.02803214 -0.02553158 -0.02298883 -0.03858549 -0.01014826 0.01495790 0.02354989
[769] 0.02211439 -0.02566655 -0.00504674 -0.01101805 -0.00692982 -0.03921325 -0.03940566 -0.00749599
[777] 0.03805929 -0.00620814 -0.00374396 -0.02325237 0.02929413 -0.01745231 0.00640088 -0.01962632
[785] -0.04124297 0.01411610 -0.01072105 -0.04168620 -0.01401384 -0.03861442 -0.03007221 -0.02177622
[793] 0.00216091 -0.01711472 -0.00183582 -0.01231198 -0.00530485 -0.02824534 -0.03609937 0.01446428
[801] -0.03790871 -0.03364501 -0.00489917 0.00168372 -0.03390708 -0.01933169 -0.03085077 -0.02672656
[809] -0.02074484 -0.00710802 -0.00882460 0.01411932 -0.08736411 -0.03426144 -0.00185984 0.01118655
[817] 0.00415453 -0.00773135 -0.09541381 -0.00557229 0.00164094 0.00695491 -0.00686187 0.00213255
[825] -0.02446441 0.00598593 -0.00113848 -0.03604497 -0.00185691 -0.01839547 -0.01518696 -0.01472526
[833] -0.01685308 -0.01192389 -0.02214717 -0.04914391 -0.04079834 0.03539863 -0.00171740 -0.03380130
[841] -0.02404942 0.01434104 -0.06482689 -0.07417141 -0.02516215 -0.00641454 -0.05277775 0.01234494
[849] -0.00884694 -0.00240308 0.01558770 -0.00946331 -0.01591890 -0.00097933 -0.01222791 0.00500122
[857] -0.03739292 -0.04379169 0.00310875 -0.00944391 -0.00369027 -0.03458154 -0.03487056 -0.00130277
[865] 0.03485286 -0.01882366 -0.02385289 -0.01841777 -0.03952611 -0.01008472 -0.03851531 -0.00089775
[873] -0.02232514 -0.00249789 -0.03292799 -0.00740002 -0.00287756 0.01614691 -0.01264950 -0.00181115
[881] -0.00624689 0.00931476 -0.02494153 -0.01776656 -0.05625175 -0.02222642 -0.01655510 -0.00704593
[889] 0.01894296 -0.00211182 -0.03411154 0.01283986 -0.04616431 0.00447970 -0.04503459 -0.01860260
[897] -0.02639233 -0.00608991 0.02223530 -0.03133591 -0.03233147 -0.00416417 -0.00527246 -0.00423827
[905] 0.04844717 -0.03769122 -0.00652885 -0.00360693 0.00549933 -0.01422108 0.01877131 0.00452042
[913] -0.03397264 -0.01017858 -0.02275584 -0.01395020 -0.00722969 -0.00690616 0.01087840 -0.02097252
[921] -0.00807237 -0.00529911 0.02903791 -0.02359836 0.00430918 0.01954801 0.03255103 0.00481096
[929] 0.01222095 -0.01531878 -0.07589472 -0.00283592 -0.00971636 -0.02628842 -0.02630826 -0.05259162
[937] 0.02363571 0.02688333 0.01848530 -0.00604214 -0.02716867 -0.02722847 -0.00685417 0.00519733
[945] -0.00065930 -0.02074074 -0.05620331 -0.04628652 0.01696365 -0.01780259 -0.02860517 -0.04008761
[953] -0.02754535 -0.01634527 -0.03466017 -0.01698841 -0.03368713 0.00137538 -0.00001726 -0.02206625
[961] -0.02937552 -0.04308455 0.01940172 0.00874241 -0.02977777 0.00698559 -0.00778609 -0.03506433
[969] -0.02745823 -0.04273358 -0.05083597 0.00930574 -0.00968700 -0.00903485 -0.00566552 0.00208223
[977] -0.00790780 -0.00700300 0.04614389 0.01499214 -0.00985071 -0.00940658 0.01978817 0.00790782
[985] -0.02914472 -0.03217463 0.00200599 -0.01098514 -0.01056695 -0.03509595 -0.02089871 0.00950951
[993] -0.04506481 -0.01342738 -0.02697804 0.01470590 -0.01251625 -0.02685997 -0.01096756 -0.01915778
[ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,4] #Synt
[1] -0.0580709 -0.1255246 -0.0531943 0.0211091 -0.0245510 -0.0957503 -0.0366260 -0.0710829 -0.1038699
[10] -0.1188227 -0.1007875 -0.0316187 -0.0501136 0.0566174 -0.0633323 -0.0683469 -0.0332834 -0.0753876
[19] 0.0208850 -0.0601255 -0.1326091 0.0113301 -0.0607656 -0.0758129 0.0405229 -0.0387246 -0.0875871
[28] -0.0138514 -0.0746292 -0.0794400 -0.0170989 -0.0328154 -0.0426362 -0.0259568 -0.0239526 -0.1126320
[37] -0.0636162 -0.0660164 -0.1617342 -0.1290503 -0.1027480 -0.0177612 -0.0337322 -0.0457715 -0.1003000
[46] -0.0518207 -0.0536642 0.0060373 0.0172356 0.0033403 -0.1160989 -0.0872466 -0.0017243 -0.1050734
[55] -0.0278275 -0.0480012 -0.0626322 -0.0910978 -0.0555417 -0.0660440 -0.0012200 -0.0066243 -0.0041009
[64] -0.0228487 0.0026215 -0.0468868 -0.0560579 0.0170095 -0.0285358 0.0096689 -0.0266034 -0.0441120
[73] -0.0300320 -0.0806484 -0.0071939 -0.0654095 -0.0047029 -0.0612530 -0.0502507 -0.0747559 -0.0191391
[82] -0.0324867 -0.0519815 -0.0173815 -0.0483023 -0.0577793 -0.0494004 -0.0273234 -0.0584371 -0.0510110
[91] -0.0589917 -0.0362908 0.0072645 0.0116599 -0.0189496 -0.0586856 -0.0488691 -0.0263996 -0.0541288
[100] -0.0636438 0.0251891 -0.0408183 -0.0339144 -0.0527506 -0.0193188 -0.0576638 -0.0659842 -0.0419608
[109] -0.0619938 -0.0508279 -0.0967825 -0.0191844 -0.0533217 -0.0677140 -0.0242431 -0.0616770 -0.0086491
[118] -0.1190887 -0.0941562 -0.0145234 -0.0840084 -0.1481985 -0.0433087 -0.0789382 -0.1013058 -0.0217750
[127] -0.0016619 -0.1587711 -0.1009086 -0.1343771 -0.0677475 -0.0759733 -0.1116960 0.0641689 -0.1010369
[136] -0.0623694 -0.1315117 -0.1002803 -0.0180661 -0.0216752 -0.0945420 -0.1175468 -0.0495235 -0.0533487
[145] -0.0949900 -0.0633932 -0.0317941 -0.0084627 0.0310388 -0.0767864 -0.0680118 -0.1259753 -0.0580258
[154] -0.0473656 -0.0623826 -0.0334204 -0.0648701 -0.0045107 -0.0505180 -0.0215068 -0.0378109 -0.0577860
[163] -0.0263118 -0.0705493 -0.0565250 -0.0120375 -0.0847457 -0.0516550 -0.0355564 -0.0800831 -0.0192013
[172] -0.0913282 -0.0019237 -0.0618620 -0.0763390 0.0224207 0.0373938 -0.0861595 -0.0546355 -0.0501233
[181] -0.0650828 0.0653112 -0.0141210 -0.0884818 0.0432456 0.0112641 -0.0843061 -0.1059385 0.0035096
[190] -0.1328496 -0.0076145 -0.0617798 -0.0695282 -0.0505623 -0.1388247 -0.1030055 -0.1089848 -0.0697045
[199] -0.0850279 -0.0159048 -0.0294539 -0.0762410 -0.0555707 -0.0258958 -0.0671594 -0.0501643 -0.0800857
[208] -0.0516575 0.0857000 -0.1400521 -0.0484127 -0.0762083 -0.0401478 -0.0008569 0.0131564 -0.0810089
[217] -0.0994654 -0.0874506 -0.0201230 -0.0539670 -0.0815648 -0.1464876 -0.1169796 -0.0950423 -0.0611003
[226] -0.0741535 0.0816886 -0.0980940 0.0192576 -0.0251506 -0.0894290 0.0014734 0.0019016 -0.0885483
[235] -0.0079345 -0.0116365 -0.0422948 -0.0937649 -0.1224406 0.0339376 0.0549601 -0.0920948 0.0001058
[244] -0.0414216 -0.0889287 -0.0829606 -0.0494142 -0.0312794 -0.0315852 -0.0991131 -0.1456220 0.0435692
[253] -0.0759001 -0.0812450 -0.0643495 -0.0622179 -0.0614507 -0.0329436 -0.0117995 0.0558435 -0.0158497
[262] -0.0913417 -0.0557749 -0.1111105 -0.0822615 -0.0159514 0.0029830 -0.0635864 -0.0359293 -0.0515109
[271] -0.0045787 -0.0450994 -0.0616897 -0.0134001 -0.0415259 -0.0963586 -0.0584049 -0.0729831 0.0546334
[280] -0.0345345 -0.0171604 -0.0542030 -0.0355484 -0.0987536 -0.0621494 -0.0270429 -0.0439867 -0.0855463
[289] -0.0077347 -0.0548824 -0.0689926 -0.0244525 -0.0495526 -0.0629574 -0.0520421 -0.0500731 0.0274207
[298] 0.0013628 -0.0103799 -0.0911320 -0.0469494 -0.0796259 -0.0732215 -0.0456949 -0.0784638 -0.1081318
[307] -0.0290121 -0.1129437 -0.0740280 -0.0607811 -0.0585021 0.0420056 -0.0445469 -0.0316670 -0.0435510
[316] -0.0286988 0.0064144 -0.0554086 -0.0260772 -0.0695439 -0.0337291 -0.0426746 -0.0459226 -0.0237162
[325] -0.0643804 -0.0340856 -0.0623693 -0.0804818 -0.0266953 -0.0875567 -0.0269529 -0.0874816 -0.0704570
[334] -0.0601661 -0.0657173 -0.0293735 -0.0516986 -0.0516427 -0.0325253 -0.1211751 -0.1092165 -0.0843056
[343] -0.1339374 -0.0667145 -0.0438018 -0.0288992 -0.1120275 -0.0407848 -0.0485029 -0.0304792 -0.0242671
[352] -0.1029410 -0.0309156 -0.1135623 -0.0634958 -0.0457185 0.0160404 0.0107945 -0.0239092 -0.0298240
[361] -0.0989963 0.0211364 -0.0823153 -0.0603631 -0.1170338 -0.0884012 -0.0651281 0.0137594 -0.0370961
[370] -0.0854204 -0.0690124 -0.0674919 -0.0152692 -0.0472213 -0.0423105 -0.0409133 -0.0037623 -0.0238612
[379] -0.0951537 -0.0939940 -0.0640982 -0.0569727 -0.0351607 -0.0639275 -0.1161663 -0.0586784 -0.1101348
[388] -0.0553361 -0.0865598 -0.1067731 0.0416231 -0.0664696 -0.0569529 -0.0165446 -0.0369655 0.0119604
[397] -0.0940155 -0.0266589 -0.0041998 -0.0525600 -0.0315629 -0.0250471 -0.0145197 -0.1054418 0.0100043
[406] 0.0010223 -0.0645040 -0.0854113 0.0294252 -0.0162946 -0.0192479 -0.0570641 -0.1231370 -0.0484145
[415] -0.0705734 -0.0670685 -0.0880902 -0.0351656 -0.0377941 -0.0564721 0.0206583 -0.1091387 -0.0366527
[424] -0.0672227 -0.1053186 -0.0750971 -0.0729764 -0.0547614 -0.0176312 -0.0480856 0.0134970 -0.0603522
[433] -0.1110494 -0.0815636 -0.0460410 0.0057996 -0.1269270 -0.0375494 -0.0127186 -0.0413153 -0.0925477
[442] 0.0056823 -0.0100834 -0.0185128 -0.0514942 -0.0949287 -0.0371059 0.0137647 0.0238620 -0.0148203
[451] -0.0147607 -0.0327465 -0.1213411 -0.0884793 -0.0054553 -0.0484142 -0.0928860 -0.0740342 -0.0707859
[460] -0.0962019 -0.0358688 -0.0349636 -0.0499158 -0.0167797 -0.0396238 -0.0461220 -0.0597468 -0.0330944
[469] -0.0663076 -0.0990223 -0.1066612 -0.0292156 -0.0742786 0.0023520 -0.0587219 -0.0415469 -0.0618586
[478] -0.0142731 -0.0314723 0.0141744 -0.0375430 0.0236752 -0.0876268 -0.0686316 -0.0097024 -0.0625698
[487] -0.0649667 0.0005447 -0.0732771 -0.1139303 -0.1406167 -0.0135691 -0.0128949 -0.0321518 -0.0325741
[496] -0.0682385 -0.0365471 -0.1306812 -0.0579546 -0.0492860 0.0278489 -0.0114363 -0.0536266 -0.0449961
[505] -0.0178958 -0.0619981 0.0017643 -0.0686702 -0.0402161 -0.0635116 -0.0086282 -0.0491020 -0.0287860
[514] -0.0009778 -0.0717235 -0.0101734 0.0311278 -0.0465415 -0.0080350 -0.0375403 -0.0984735 -0.0316285
[523] -0.0643713 -0.1292523 -0.1463736 -0.0631842 -0.0494830 -0.0902800 -0.1149235 -0.0895128 -0.0297794
[532] -0.0765637 0.0372551 -0.0306406 -0.0828848 -0.0136323 -0.0286491 -0.1016975 -0.0580203 -0.0027378
[541] -0.0989857 -0.0418428 -0.0890605 -0.1246836 -0.0289774 0.0091468 -0.0028894 -0.0685231 -0.0757349
[550] -0.0423850 -0.0342308 -0.1517858 -0.0391310 0.0083874 -0.0776377 -0.0381384 -0.0921305 -0.0753580
[559] -0.0853246 -0.0649890 -0.0433499 -0.0283672 -0.0861330 -0.0787500 -0.0797864 -0.0419311 -0.0873742
[568] -0.0595298 -0.0904336 -0.0338434 -0.1120734 -0.0941751 -0.0471597 -0.0347115 -0.0551496 0.0166354
[577] -0.0444797 -0.0767432 -0.0460097 -0.0047036 -0.0377310 -0.0888965 -0.0056026 -0.0838246 0.0071702
[586] -0.0915864 -0.0191924 0.0118730 -0.1080674 0.0145589 0.0001870 -0.0416570 -0.0264733 -0.0479717
[595] -0.0483762 -0.1190131 -0.0117415 -0.0214343 0.0022974 -0.0759233 -0.0629609 -0.0555673 0.0058426
[604] -0.0936200 -0.0077048 -0.0308749 -0.0507123 0.0521713 -0.0583258 -0.0118998 -0.0404513 -0.0962759
[613] -0.0629154 -0.0771383 -0.0667545 -0.0943365 -0.0284222 -0.0934684 -0.0467909 -0.0964143 -0.0752365
[622] 0.0028092 -0.0308024 -0.0777274 -0.0615282 -0.0728961 -0.0078966 -0.0728099 -0.0428642 -0.1239330
[631] -0.0306936 -0.0852184 -0.0968696 0.0463589 -0.0556192 -0.0650360 -0.0753129 -0.0225595 -0.0491702
[640] 0.0076265 -0.0575809 -0.0269407 -0.0854585 -0.0548602 0.0071432 -0.0680956 -0.0810258 -0.0262273
[649] -0.0179745 -0.0924814 0.0092550 -0.0882873 -0.1045615 -0.0586414 -0.0372997 -0.0372705 -0.0891078
[658] -0.0335533 -0.0757536 -0.0183747 0.0126745 -0.0574951 -0.0078025 -0.0418091 0.0039441 -0.0175751
[667] -0.0499059 0.0133153 -0.0265012 -0.0600198 -0.0675733 -0.0824336 -0.0523336 -0.0530426 -0.0221714
[676] -0.1632830 -0.0617365 -0.0866244 -0.0554014 -0.0530485 0.0018667 -0.0581943 -0.0529550 0.0198446
[685] -0.0825239 -0.0410522 0.0275531 -0.0537602 -0.1160113 -0.0678019 -0.0228692 -0.0595094 -0.0199657
[694] -0.0319110 -0.0295339 -0.1306761 -0.0030895 -0.0408108 -0.0564524 -0.0594559 -0.0170114 -0.0582647
[703] -0.0910905 -0.1379280 -0.0118500 -0.1256541 0.0137677 -0.0317303 -0.0487279 -0.0260480 -0.0034782
[712] -0.0243240 -0.0935811 -0.0138758 -0.0525151 -0.0512904 -0.1513964 -0.0825594 -0.0383921 -0.0849249
[721] -0.0812503 -0.0809393 -0.0357952 -0.0639331 -0.0524798 -0.0215323 0.0074074 -0.0426281 -0.1129249
[730] -0.0683460 -0.0330312 -0.0469217 -0.0950688 -0.1139316 -0.0834383 -0.1378749 -0.0213169 -0.1347814
[739] -0.0772514 -0.0228314 -0.0360555 -0.0797061 -0.0251391 -0.0315608 -0.1080079 -0.1331927 -0.0721431
[748] 0.0120728 -0.0580965 -0.0108570 -0.0542894 -0.0558066 0.0271923 -0.0840641 -0.0635376 -0.0471780
[757] 0.0030720 0.0107691 -0.0649546 -0.0167415 -0.0375110 -0.0382323 -0.0863283 -0.0425962 -0.0473345
[766] -0.0538690 -0.0423341 -0.0646675 -0.0137288 -0.0082135 -0.0765607 -0.0802003 -0.0485499 -0.0777823
[775] -0.1231128 -0.0483810 -0.0640275 -0.0692970 -0.0142725 -0.0540057 0.0254805 -0.0628860 -0.1037064
[784] -0.0331070 -0.0202918 -0.0898621 -0.0635626 -0.0144646 -0.0639838 -0.0012904 -0.0179106 -0.0208269
[793] -0.0426377 -0.0146822 -0.0104251 -0.0336804 -0.0833945 -0.0997342 -0.1032926 -0.0899683 -0.1100991
[802] -0.0190055 -0.0492793 -0.0420937 0.0052866 -0.0805461 -0.0617757 -0.0258974 -0.0192520 -0.0423604
[811] 0.0410904 -0.0436443 -0.1056956 -0.0469403 -0.1172835 -0.0189209 -0.0151243 -0.0239779 -0.1144203
[820] -0.0451806 -0.0488542 -0.0515097 -0.0721758 -0.0559085 -0.0695870 -0.0910261 -0.0806343 -0.1650156
[829] -0.0366731 -0.0422207 -0.0147527 -0.0911082 -0.0503074 -0.1035620 -0.0524144 -0.0984727 -0.0036500
[838] -0.0076330 -0.1016605 -0.0513939 -0.0823356 -0.0481003 -0.0990802 -0.0163779 -0.0569816 -0.1179487
[847] -0.0492650 -0.1454959 -0.1040295 -0.1018497 -0.0051481 -0.1107469 -0.0377337 -0.0480553 -0.0152915
[856] -0.0004498 -0.1055547 -0.0461308 -0.0001680 -0.0476518 -0.1362110 -0.0280942 -0.0418220 -0.1076138
[865] -0.0064124 0.0363456 -0.1036619 -0.0171969 -0.0835054 -0.0893393 -0.0567807 -0.0604021 -0.1331360
[874] 0.0099883 -0.0682999 -0.0310496 -0.0135102 -0.0746947 -0.0650920 -0.0830484 -0.0338526 -0.0573135
[883] -0.0102278 -0.0349917 -0.0510138 -0.0163819 -0.0736709 -0.0946622 -0.1052607 -0.1272375 -0.0022206
[892] -0.0291186 -0.0818223 -0.0257542 -0.0053992 -0.0646445 -0.0170949 0.0060151 -0.0395360 -0.0805634
[901] -0.0748053 -0.0618500 -0.0535615 -0.0536990 -0.0398397 -0.0049859 -0.0360675 -0.0533977 -0.1031987
[910] -0.0394403 -0.0434704 -0.0573012 -0.0514706 -0.0866545 -0.0231664 -0.0150252 -0.0396506 -0.0364783
[919] -0.0480184 -0.1260701 -0.1235779 -0.0573749 -0.0297702 -0.0568597 -0.0452084 -0.0532208 -0.0280845
[928] -0.0802876 -0.0633139 -0.0080881 -0.0445927 -0.0537926 -0.0233476 -0.0409836 -0.0238367 -0.0459161
[937] -0.0742592 -0.0587913 -0.0198683 -0.0343025 -0.0732501 -0.0268379 0.0051605 -0.0485064 -0.0556865
[946] -0.0601430 -0.0384196 -0.0653317 -0.0369426 0.0270919 -0.1234232 -0.0168269 -0.0201091 -0.0339656
[955] -0.0782871 -0.0244635 -0.0515288 -0.0110918 -0.0381850 -0.0052963 -0.0744787 -0.0893083 -0.0543988
[964] -0.0380151 -0.0468271 -0.0922156 -0.0428788 -0.0338061 -0.0508924 -0.0672524 0.0097903 -0.0471493
[973] -0.0751957 -0.0475501 -0.0759731 -0.0695680 0.0248479 -0.0006723 -0.1135022 -0.1093690 -0.0366890
[982] -0.0736169 -0.0385838 -0.1455670 -0.0756360 -0.0541503 -0.0207080 -0.0313012 -0.1082914 -0.0817355
[991] -0.0368730 -0.0309640 0.0412238 -0.0693011 -0.0494924 -0.0848390 -0.0290980 -0.0425895 -0.0232912
[1000] -0.0520950
[ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,5] #Lex
[1] -0.069098 -0.116367 -0.124565 -0.176420 -0.093531 -0.116993 -0.144602 -0.122323 -0.156137 -0.085809
[11] -0.112633 -0.055190 -0.011962 -0.140794 -0.139883 -0.083894 -0.122228 -0.095080 -0.143504 -0.095749
[21] -0.088869 -0.074267 -0.100710 -0.078606 -0.154985 -0.140247 -0.154963 -0.139098 -0.126072 -0.111806
[31] -0.078000 -0.112447 -0.092223 -0.150971 -0.107237 -0.066879 -0.131230 -0.019444 -0.063071 -0.045208
[41] -0.057779 -0.104169 -0.172058 -0.122537 -0.102102 -0.078335 -0.136413 -0.163440 -0.165446 -0.075063
[51] -0.040287 -0.116522 -0.152285 -0.086492 -0.193180 -0.156147 -0.135968 -0.154164 -0.112217 -0.111301
[61] -0.131721 -0.141640 -0.095810 -0.149869 -0.117142 -0.060183 -0.142303 -0.179804 -0.130077 -0.183636
[71] -0.138277 -0.125884 -0.137317 -0.046757 -0.148556 -0.139671 -0.175938 -0.124494 -0.117983 -0.077199
[81] -0.165997 -0.175225 -0.114825 -0.131158 -0.104889 -0.094946 -0.016849 -0.130537 -0.105199 -0.158697
[91] -0.075878 -0.138758 -0.127239 -0.081171 -0.204199 -0.099366 -0.135095 -0.113480 -0.127146 -0.130908
[101] -0.126751 -0.140049 -0.067722 -0.082295 -0.094392 -0.094218 -0.149414 -0.124576 -0.116494 -0.126512
[111] -0.149175 -0.102982 -0.088846 -0.127740 -0.084759 -0.112960 -0.056006 -0.094348 -0.065176 -0.147071
[121] -0.162986 -0.088359 -0.091713 -0.129109 -0.120877 -0.151758 -0.123199 -0.137083 -0.072490 -0.058929
[131] -0.126569 -0.065289 -0.088674 -0.152853 -0.097695 -0.098881 -0.056658 -0.110842 -0.096446 -0.156610
[141] -0.045022 -0.044527 -0.120854 -0.135080 -0.056219 -0.120617 -0.117988 -0.156476 -0.127451 -0.095089
[151] -0.139622 -0.048504 -0.147525 -0.152364 -0.068968 -0.123273 -0.067668 -0.170196 -0.139110 -0.143617
[161] -0.040625 -0.076441 -0.134336 -0.102612 -0.122604 -0.144819 -0.138918 0.007022 -0.159193 -0.144185
[171] -0.095042 -0.096850 -0.179706 -0.125446 -0.106241 -0.141298 -0.157819 -0.059944 -0.171304 -0.116033
[181] -0.144794 -0.223248 -0.118792 -0.104402 -0.069710 -0.177247 -0.107289 -0.094903 -0.115140 -0.127881
[191] -0.122409 -0.062454 -0.066658 -0.155928 -0.102893 -0.069690 -0.149533 -0.103472 -0.087585 -0.065465
[201] -0.122360 -0.070461 -0.082132 -0.127566 -0.072200 -0.170826 -0.137336 -0.092408 -0.128416 -0.092785
[211] -0.138566 -0.146117 -0.161052 -0.095728 -0.180499 -0.138197 -0.037619 -0.089301 -0.146439 -0.140211
[221] -0.072404 -0.117175 -0.069755 -0.107917 -0.077235 -0.055443 -0.156196 -0.038115 -0.120134 -0.128018
[231] -0.123712 -0.175368 -0.175735 -0.109667 -0.101682 -0.099033 -0.152713 -0.054651 -0.084967 -0.168308
[241] -0.174038 -0.081800 -0.091868 -0.084585 -0.122413 -0.137865 -0.137064 -0.116848 -0.007885 -0.105092
[251] -0.115108 -0.138097 -0.044377 -0.089559 -0.070295 -0.108877 -0.032250 -0.183496 -0.099876 -0.211644
[261] -0.034692 -0.158677 -0.109249 -0.069533 -0.099716 -0.187962 -0.177633 -0.106010 -0.141056 -0.169463
[271] -0.133946 -0.152625 -0.081062 -0.154919 -0.098120 -0.081864 -0.102095 -0.098583 -0.132146 -0.124000
[281] -0.065467 -0.121637 -0.052192 -0.011481 -0.094977 -0.145679 -0.127279 -0.109214 -0.150877 -0.159692
[291] -0.161525 -0.171950 -0.075468 -0.100357 -0.044047 -0.096893 -0.084282 -0.181009 -0.165792 -0.104579
[301] -0.102356 -0.093110 -0.134265 -0.116732 -0.155410 -0.082044 -0.097050 -0.139107 -0.068881 -0.112775
[311] -0.170252 -0.070816 -0.115779 -0.139770 -0.108304 -0.093576 -0.124749 -0.147805 -0.194439 -0.093760
[321] -0.158358 -0.115964 -0.179090 -0.111356 -0.147398 -0.124595 -0.116036 -0.114652 -0.074998 -0.058483
[331] -0.147456 -0.105224 -0.130534 -0.107753 -0.080887 -0.122855 -0.126424 -0.125247 -0.115554 -0.082291
[341] -0.086678 -0.096080 -0.108873 -0.106874 -0.046461 -0.083879 -0.082836 -0.128702 -0.154181 -0.082319
[351] -0.107059 -0.120464 -0.099665 -0.100041 -0.150055 -0.074635 -0.170327 0.006105 -0.108806 -0.122475
[361] -0.055386 -0.197177 -0.092819 -0.069671 -0.108020 -0.140051 -0.113065 -0.154787 -0.100930 -0.083619
[371] -0.057773 -0.117473 -0.132861 -0.092716 -0.128280 -0.042223 -0.155005 -0.097156 -0.099035 -0.065588
[381] -0.069124 -0.159454 -0.120879 -0.068677 -0.157738 -0.089619 -0.052432 -0.114687 -0.124194 -0.152423
[391] -0.146954 -0.050868 -0.152589 -0.123008 -0.115726 -0.135665 -0.098199 -0.190340 -0.111083 -0.143152
[401] -0.132259 -0.125619 -0.133972 -0.026739 -0.138902 -0.117914 -0.110430 -0.103129 -0.130369 -0.184670
[411] -0.113156 -0.115579 -0.101403 -0.145838 -0.041026 -0.040356 -0.150894 -0.113499 -0.081575 -0.116458
[421] -0.186911 -0.049350 -0.169083 -0.109962 -0.078816 -0.145057 -0.084698 -0.144987 -0.111827 -0.052508
[431] -0.159121 -0.086030 -0.132064 -0.119283 -0.212735 -0.151569 -0.067665 -0.066687 -0.147534 -0.124278
[441] -0.066118 -0.157946 -0.184822 -0.141101 -0.148307 -0.144094 -0.158824 -0.192085 -0.110417 -0.115749
[451] -0.140390 -0.092111 -0.082500 -0.105898 -0.104195 -0.128391 -0.124721 -0.137544 -0.103926 -0.122176
[461] -0.015579 -0.072952 -0.140538 -0.165148 -0.185463 -0.135608 -0.097107 -0.060435 -0.021274 -0.122633
[471] -0.106152 -0.096968 -0.039941 -0.093340 -0.143502 -0.099747 -0.119122 -0.072401 -0.176169 -0.140081
[481] -0.144500 -0.109760 -0.112538 -0.065794 -0.078026 -0.138208 -0.062519 -0.141922 -0.130767 -0.065247
[491] -0.118856 -0.143466 -0.182852 -0.128671 -0.175253 -0.172703 -0.093260 -0.108746 -0.086384 -0.178275
[501] -0.145730 -0.049612 -0.152779 -0.117684 -0.183302 -0.132725 -0.131466 -0.081131 -0.118209 -0.094710
[511] -0.209347 -0.149631 -0.161049 -0.141967 -0.085301 -0.127151 -0.144510 -0.143535 -0.184263 -0.174404
[521] -0.096924 -0.079024 -0.113099 -0.026106 -0.147682 -0.129701 -0.119056 -0.073954 -0.105948 -0.188146
[531] -0.096938 -0.092715 -0.145587 -0.105246 -0.089751 -0.155716 -0.169169 -0.076096 -0.107101 -0.204504
[541] -0.019519 -0.141736 -0.101414 -0.089609 -0.134715 -0.170632 -0.124851 -0.101802 -0.132508 -0.085012
[551] -0.187648 -0.025306 -0.125212 -0.169976 -0.084018 -0.120062 -0.137456 -0.108751 -0.149719 -0.109713
[561] -0.145995 -0.131225 -0.068013 -0.096919 -0.102522 -0.090263 -0.127192 -0.144362 -0.102319 -0.041916
[571] -0.079321 -0.063572 -0.101071 -0.172347 -0.169643 -0.147326 -0.151813 -0.110167 -0.074019 -0.145063
[581] -0.121036 -0.145256 -0.168465 -0.070203 -0.170692 -0.051954 -0.080308 -0.128755 -0.132380 -0.108236
[591] -0.123430 -0.135819 -0.061696 -0.063155 -0.111409 -0.192680 -0.136550 -0.023158 -0.048584 -0.088090
[601] -0.083681 0.003982 -0.173000 -0.056182 -0.126635 -0.151683 -0.086074 -0.186393 -0.056276 -0.107955
[611] -0.074466 -0.055978 -0.110506 -0.125348 -0.141874 -0.082598 -0.142706 -0.152668 -0.105907 -0.051033
[621] -0.115839 -0.044170 -0.209931 -0.106288 -0.131804 -0.122321 -0.119186 -0.149995 -0.131558 -0.058133
[631] -0.106718 -0.104906 -0.143172 -0.148841 -0.125667 -0.065137 -0.132165 -0.138331 -0.077691 -0.158182
[641] -0.068526 -0.139713 -0.080221 -0.142450 -0.126029 -0.150428 -0.153236 -0.134201 -0.141065 -0.030868
[651] -0.185758 -0.098862 -0.094789 -0.135163 -0.077312 -0.153485 -0.101439 -0.071731 -0.067044 -0.103930
[661] -0.151210 -0.139346 -0.090040 -0.159072 -0.158595 -0.168187 -0.143937 -0.116629 -0.136383 -0.058703
[671] -0.112681 -0.122869 -0.105477 -0.108287 -0.118301 -0.095424 -0.153006 -0.112280 -0.073689 -0.045380
[681] -0.152159 -0.116220 -0.118116 -0.229255 -0.048394 -0.087643 -0.207431 -0.132912 -0.096813 -0.029010
[691] -0.136544 -0.090841 -0.060851 -0.086266 -0.166369 -0.080622 -0.090513 -0.118800 -0.182897 -0.041249
[701] -0.110380 -0.130980 -0.204663 -0.080105 -0.159053 -0.062329 -0.143764 -0.124914 -0.113763 -0.138006
[711] -0.081738 -0.113786 -0.053474 -0.142461 -0.095527 -0.102213 0.000860 -0.065075 -0.052158 -0.128032
[721] -0.058360 -0.075632 -0.097314 -0.070790 -0.124074 -0.089446 -0.098695 -0.105144 -0.025761 -0.126140
[731] -0.032964 -0.105709 -0.046161 -0.091076 -0.065558 -0.087558 -0.107808 -0.030521 -0.046847 -0.081190
[741] -0.123736 -0.111015 -0.153036 -0.125532 -0.165401 -0.061270 -0.079094 -0.163678 -0.201571 -0.113225
[751] -0.058136 -0.146035 -0.100117 -0.146731 -0.130332 -0.147020 -0.154254 -0.170282 -0.104872 -0.141220
[761] -0.124929 -0.143690 -0.109699 -0.114132 -0.097816 -0.142072 -0.086402 -0.115846 -0.103915 -0.094714
[771] -0.091562 -0.079969 -0.188586 -0.048380 -0.093601 -0.061874 -0.176336 -0.087420 -0.150279 -0.095198
[781] -0.151466 -0.072720 -0.094956 -0.097437 -0.212711 -0.121827 -0.142938 -0.113769 -0.077620 -0.163314
[791] -0.136774 -0.019915 -0.148913 -0.112889 -0.119581 -0.089430 -0.096565 -0.066426 -0.091519 -0.117582
[801] -0.067381 -0.160887 -0.046652 -0.132583 -0.153491 -0.131491 -0.111374 -0.126493 -0.050485 -0.089691
[811] -0.128448 -0.071336 -0.087850 -0.171133 -0.148290 -0.120115 -0.071404 -0.117494 -0.072152 -0.130735
[821] -0.136963 -0.110370 -0.130002 -0.088247 -0.116334 -0.094657 -0.039650 -0.026471 -0.148814 -0.127841
[831] -0.140511 -0.129164 -0.067736 -0.042645 -0.164376 -0.051087 -0.103615 -0.139784 -0.053773 -0.097075
[841] -0.149658 -0.011753 -0.120016 -0.099116 -0.107369 -0.159037 -0.197443 -0.105952 -0.073051 -0.117559
[851] -0.131199 -0.103488 -0.142457 -0.068585 -0.107818 -0.094705 -0.041710 -0.124898 -0.086966 -0.090774
[861] -0.019987 -0.146838 -0.175867 -0.061864 -0.169124 -0.202551 -0.098080 -0.027220 -0.102020 -0.125510
[871] -0.138921 -0.163541 -0.110454 -0.176972 -0.144316 -0.069125 -0.146512 -0.036729 -0.055412 -0.028038
[881] -0.127364 -0.134999 -0.075482 -0.124951 -0.104623 -0.157149 -0.145363 -0.130062 -0.070864 -0.074599
[891] -0.151363 -0.123065 -0.086094 -0.106750 -0.117897 -0.130176 -0.138050 -0.111307 -0.159653 -0.034365
[901] -0.051216 -0.135901 -0.059939 -0.126474 -0.136822 -0.131682 -0.130941 -0.117870 -0.090600 -0.137274
[911] -0.178629 -0.018405 -0.131559 -0.146985 -0.101355 -0.061634 -0.109037 -0.044171 -0.115071 -0.077297
[921] -0.076888 -0.094813 -0.107897 -0.076449 -0.130404 -0.045173 -0.120664 -0.075043 -0.012034 -0.142059
[931] -0.114466 -0.031755 -0.155769 -0.103752 -0.046563 -0.078957 -0.072355 -0.082444 -0.133971 -0.142465
[941] -0.074725 -0.126174 -0.146304 -0.147849 -0.080413 -0.118043 -0.118303 -0.093764 -0.175888 -0.138776
[951] -0.075901 -0.138565 -0.107713 -0.080652 -0.099531 -0.102432 -0.108291 -0.099210 -0.107402 -0.090275
[961] -0.079868 -0.125617 -0.144289 -0.130939 0.028052 -0.097770 -0.166373 -0.083253 -0.121260 -0.163023
[971] -0.170044 -0.140098 -0.035504 -0.164303 -0.092125 -0.168608 -0.149066 -0.112349 -0.095561 0.014181
[981] -0.143994 -0.084143 -0.135315 -0.074579 -0.144324 -0.104185 -0.180882 -0.121829 -0.116332 -0.093875
[991] -0.140605 -0.185408 -0.151596 -0.097202 -0.091434 -0.051397 -0.115125 -0.114293 -0.149513 -0.129991
[ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,6] #Gram_x_Synt
[1] -0.012053919 0.014754522 0.007144885 0.003049790 -0.011411235 -0.015348895 -0.009031572
[8] 0.009039362 0.016592231 0.020419034 -0.006198488 -0.007743556 0.007777367 -0.002665639
[15] 0.009880354 0.010274372 -0.027354002 -0.021811222 0.032081467 0.015904213 0.021663822
[22] -0.047943630 0.064699402 -0.003641274 0.016484393 -0.001865889 -0.001151370 -0.015335693
[29] 0.033256514 -0.007083530 0.019777815 -0.018994960 -0.019971223 0.031737706 0.014013200
[36] 0.029146026 0.014526868 0.023840107 0.007806686 0.008982127 0.029482104 0.029805211
[43] 0.000862000 0.047751829 0.004780964 0.000361097 0.009224333 0.015614542 0.002960787
[50] 0.002811210 0.000607831 0.000250492 -0.017020925 0.002097159 0.026120180 0.042016801
[57] 0.028003300 0.074489403 -0.010505122 0.033234996 0.010848753 0.034818311 -0.006707679
[64] -0.005343586 -0.054321958 -0.004069811 -0.046587841 0.030966741 0.006207130 0.013901873
[71] -0.002321440 0.004574734 0.022767526 0.001576154 -0.033016234 0.014800641 0.024906337
[78] 0.031443095 -0.021030220 0.010658900 -0.009478767 0.007670494 -0.020670162 0.067108352
[85] -0.024463112 0.044925778 -0.021333040 0.011518457 0.039572143 -0.008955921 0.014621281
[92] 0.015930458 -0.001467913 -0.010837968 -0.054418834 0.013063912 0.011902387 0.003953942
[99] -0.015386521 -0.009284926 0.037822066 0.052793045 0.020167610 0.021943492 0.037433486
[106] -0.001596520 0.042264640 0.024495051 0.009167192 0.026045424 0.012572089 0.006028263
[113] -0.016993474 0.002136769 0.023893247 0.034608592 0.018427484 -0.006040735 0.012260518
[120] 0.009591331 0.009626748 0.036799164 0.010020204 0.023910852 0.031850980 0.013758085
[127] 0.005467012 -0.020167694 0.008634891 0.023729991 0.046289298 -0.006157384 -0.000566425
[134] 0.032965204 0.031391943 0.051088323 0.019522407 0.050655783 -0.028020993 -0.018016362
[141] 0.005923816 -0.015911805 -0.015151022 0.020693562 0.012625658 0.049636517 0.024671339
[148] -0.010663026 0.010876388 0.006438925 0.023421056 -0.011364867 0.042585596 0.048486604
[155] 0.031199181 -0.006564058 0.019550293 0.017265632 0.048816281 -0.054881252 -0.002269258
[162] 0.005412218 0.029061996 0.006378123 -0.019637594 0.026610881 0.009251373 0.056563325
[169] 0.005212768 0.030157807 -0.005499728 0.002358558 0.008711667 0.001641390 0.021277651
[176] -0.024797339 0.021848739 0.013811629 0.015025104 0.006561826 0.046791937 0.044749528
[183] -0.027967058 0.014661229 0.050920277 0.025678659 -0.024421968 -0.000666629 0.010964989
[190] 0.030398099 0.010293043 -0.005542221 0.000389019 -0.011368802 -0.008085105 0.011678237
[197] -0.005438898 0.006733277 0.021156387 0.036478931 -0.005149817 0.005196897 -0.009360939
[204] 0.008200879 0.008228476 0.007929707 0.028210856 0.025038587 0.013782165 0.046781534
[211] 0.050445304 0.012528888 -0.000753913 -0.035657174 0.005037405 0.050636999 0.029250586
[218] 0.021235383 0.000724994 0.034776776 -0.027406108 0.017014528 0.013169300 0.024859776
[225] 0.034634536 0.011830101 -0.010510100 0.017310855 0.020732427 0.018361981 0.049341755
[232] 0.038117915 0.041547190 0.041586174 0.043954156 0.018320186 0.010922747 -0.005859799
[239] 0.006724215 0.002060250 0.023915412 0.015623998 -0.018653858 0.015604860 -0.016343412
[246] 0.050553638 0.014991292 0.014199034 0.044053953 -0.003562401 0.020257409 0.017183454
[253] 0.008845914 0.045592271 0.006887554 0.003949070 0.061227832 0.006664268 0.003188033
[260] 0.012771630 0.022009213 0.016744541 -0.000238822 0.008166556 -0.013924808 0.006805684
[267] 0.003817505 -0.019192657 0.021892371 0.049597519 -0.001202841 0.021979227 0.004517902
[274] 0.019087386 0.022100085 -0.011092042 0.007058215 0.037117740 0.023837507 0.008817305
[281] -0.013990823 -0.006241586 -0.003890364 -0.034854885 0.042098293 -0.002868521 -0.013580604
[288] 0.040512306 -0.004801704 0.004974189 0.006968378 0.000102830 0.003127398 -0.023633128
[295] 0.033897042 -0.012044675 0.007065881 -0.003874734 -0.000754462 0.015776918 0.048706165
[302] 0.022663981 0.020159756 0.022704555 -0.008801243 -0.023002472 0.004670624 0.023382642
[309] -0.000852052 -0.020165830 0.011195817 0.009162465 -0.001099382 -0.021077353 0.022289281
[316] -0.051627648 0.000157403 0.070222834 -0.006244143 0.019957914 0.030901458 0.003225820
[323] -0.007268060 -0.003484104 0.023939353 0.038905545 -0.005255513 0.042758748 0.000372006
[330] 0.015141637 -0.009956753 0.026870998 0.007675974 0.008967939 0.024424123 0.020832624
[337] 0.026632741 -0.012873898 -0.014803293 0.026098420 0.039664665 -0.003050198 0.017589908
[344] 0.007170390 0.025028954 -0.005563575 0.037158467 0.005980765 0.017965216 -0.012339523
[351] 0.003346277 0.025558752 -0.010826337 0.015820284 -0.003262820 0.032833077 0.001429373
[358] 0.044786861 -0.030918742 -0.002428887 0.004334986 0.006103044 0.005175016 0.030853259
[365] 0.007642248 -0.022361483 0.019205251 0.044242301 -0.026414495 -0.010922628 0.006345946
[372] 0.001152588 0.008882038 0.007843064 -0.002538221 0.014874287 -0.001480009 -0.017798511
[379] 0.014858787 0.030301098 0.037023514 -0.017214374 0.039558185 0.056279300 0.007367182
[386] -0.006101727 -0.005720232 -0.013748114 -0.010756730 0.007551587 0.006368365 -0.004272900
[393] 0.006911902 0.010567079 0.016096658 -0.015405292 -0.005442688 -0.004323465 0.009408704
[400] 0.047856718 0.007483488 0.007998062 0.006371931 -0.011529029 0.004587191 0.044781677
[407] 0.023354210 0.033594966 0.044104965 -0.008467016 -0.005118294 0.033089599 0.015013548
[414] 0.039686870 -0.009661252 0.031851627 0.019284280 0.002436709 -0.009942009 0.025920021
[421] 0.023419027 0.006242764 -0.000823480 -0.020710263 0.016774499 0.013601631 0.069076175
[428] -0.028404506 -0.001806817 0.019743867 -0.000571089 -0.008219648 0.008236665 0.028811928
[435] 0.024859711 0.032736719 0.029943147 0.011351226 0.002291657 0.020581127 0.014001618
[442] 0.014934706 -0.032167873 0.003983736 0.024181380 0.035178239 0.012927366 0.039760399
[449] -0.005629029 0.015405629 0.012123241 -0.018592560 0.002181511 0.015171355 -0.013936037
[456] -0.004944377 0.007431961 0.034116533 0.013772340 -0.006508610 -0.013663091 -0.012728986
[463] 0.044357768 0.054914802 -0.013295729 0.000796607 0.055135899 0.038053120 -0.010750937
[470] -0.003407293 0.048667024 0.039655318 0.028360000 -0.011139553 -0.018388722 0.011541109
[477] 0.007668721 0.007457710 0.025817415 0.019856628 0.003284943 -0.015616648 -0.012828690
[484] 0.062811535 -0.019392346 0.035210502 0.045772524 0.051318771 -0.011890894 -0.020404882
[491] 0.005469225 0.006292418 -0.000006148 0.029995801 0.028303511 -0.014172149 0.018505936
[498] 0.049635036 -0.002715721 -0.023563702 0.000396551 0.057023143 0.037678222 0.004514297
[505] -0.014472306 0.031057672 0.017058218 0.041565510 -0.001717792 0.038301211 0.071781959
[512] 0.032048461 -0.025011325 0.020458530 -0.002143665 0.039278700 0.021704579 -0.008806822
[519] 0.018869992 -0.023280891 0.038901766 0.026530751 0.006381008 0.060991905 0.032556930
[526] -0.031139362 -0.016647832 0.058942433 0.022156816 0.004503064 0.056212538 -0.034383235
[533] 0.014465665 0.019392152 -0.003466331 0.014388073 0.006884957 0.009225298 0.015329753
[540] 0.011903942 0.009005449 0.013453775 -0.013199192 0.036351493 0.045748677 0.026582619
[547] 0.024651536 -0.037889066 0.034040895 0.008216849 -0.029054037 -0.019732689 0.012183848
[554] 0.013898221 0.041247424 0.024024532 0.066441407 0.031490212 -0.005688212 0.003497844
[561] -0.035014175 -0.002662352 0.014257729 0.029548115 0.016740242 0.031869100 0.004848812
[568] -0.004331868 0.004338056 -0.018475191 -0.047282759 0.003644762 0.014951040 0.026167329
[575] 0.043889880 0.024999386 0.002589263 0.021193174 0.016907587 0.034450189 0.018299780
[582] -0.001110952 0.022989705 -0.016146686 0.039457983 0.063214249 0.041375807 0.003579335
[589] -0.006704866 -0.002280280 -0.016583614 0.053267314 -0.020037257 -0.006118687 0.005912616
[596] 0.050662193 0.028214021 0.009168763 0.018450950 -0.021918623 -0.001668109 0.036464723
[603] 0.015674621 0.001316117 -0.015252696 -0.019400787 0.012349184 0.004159622 0.003293789
[610] 0.026441957 0.043647602 0.030932162 -0.016300561 0.035343431 0.031774570 0.013181647
[617] 0.005420667 -0.017318452 0.003509716 0.033422271 0.025982079 0.039929521 0.038821851
[624] -0.018944550 0.016694112 0.006658130 0.015950420 0.009074993 0.005294001 -0.003015660
[631] 0.015526018 -0.006717745 0.003552571 -0.018544149 -0.005691882 0.020992950 -0.012617800
[638] 0.022345279 -0.000039778 -0.010707206 0.021541263 0.042733030 0.011347959 -0.007234615
[645] 0.039906873 -0.013909065 0.006361666 0.020702706 -0.006860667 0.031063582 0.066136145
[652] 0.048047799 0.032720913 0.002822924 0.023648236 0.007544186 0.018143277 0.020396678
[659] 0.016838428 0.000498562 0.034176210 0.013587618 0.035882788 0.028280884 0.017033557
[666] 0.020445896 0.011110946 0.000036181 0.026962838 -0.005930062 0.028811710 0.039446787
[673] 0.029749166 0.001960003 0.022929681 -0.005712091 0.034676024 -0.017238645 0.031562640
[680] 0.003815196 0.015994894 0.011774437 0.018343626 0.023858496 0.019682342 0.015071338
[687] 0.048596945 0.027659239 0.065850736 -0.037221541 0.047056788 0.029936660 0.050982556
[694] 0.023943165 -0.021755629 0.002741206 0.002990121 0.032345890 0.021159455 0.034310716
[701] 0.040598341 -0.008942137 0.023064678 0.035793727 0.039702970 -0.002390951 0.005644896
[708] 0.009629626 -0.033680237 0.030776276 0.001536231 0.001253315 0.004417287 0.017071821
[715] 0.035959268 0.009330723 0.023285293 -0.004454930 -0.020623033 0.026082472 -0.022438432
[722] 0.008329827 0.025014950 0.026363217 -0.012304137 0.049365428 0.019805067 0.052183807
[729] -0.008430330 0.040307198 -0.018826765 0.016490785 0.061984964 0.005365587 0.044444357
[736] 0.018585105 -0.003226290 0.021049175 -0.010551526 0.001663555 0.048180334 0.048653505
[743] 0.015090386 -0.010431401 0.029503216 0.006924384 0.008781463 0.004555504 0.000042913
[750] 0.017933355 0.031648791 -0.004329365 -0.045191145 0.040187157 0.072329904 0.014598781
[757] -0.003412739 0.002453786 0.043264804 0.009777457 -0.007603551 0.027443990 0.042178667
[764] 0.030974086 0.034262699 0.008403341 -0.036547959 0.040874872 0.035146108 0.018878237
[771] -0.043905149 0.038710184 0.028068466 0.022390705 0.029764010 0.057009173 0.045727026
[778] 0.012067397 -0.006341294 0.000468020 0.043068943 -0.005756593 -0.025453610 0.034412938
[785] 0.021323645 -0.001644282 0.004806446 -0.000568906 0.010519139 0.009240913 -0.002436806
[792] -0.021200304 -0.024830389 0.036075209 -0.014899199 -0.012150831 0.002999114 0.002242233
[799] 0.039615899 -0.003795963 -0.034565161 0.029131107 0.028073597 0.015484004 -0.032441500
[806] -0.009185958 -0.030228665 0.002726402 -0.017533839 0.003029689 0.028433474 0.041672016
[813] 0.020215372 -0.003961926 0.029731908 -0.024040580 -0.003882222 0.049569214 0.036364653
[820] 0.034613451 0.006963568 0.026591667 -0.038113123 0.027112555 0.021861104 -0.002479497
[827] 0.021054882 0.023035508 -0.004582576 -0.025667750 0.018828927 0.019623567 0.004891520
[834] 0.019820957 0.049042078 -0.021253791 0.032418990 -0.005225294 0.008422118 0.019205023
[841] 0.028316707 0.027250070 0.003329716 0.002001136 0.028099533 0.034584155 -0.043788834
[848] 0.006555839 0.047865522 0.037586149 -0.043947864 0.017350364 0.012340980 0.035062414
[855] 0.034300392 -0.018176239 0.002475874 0.042239447 -0.018509427 -0.009978129 0.019361764
[862] -0.014064001 0.037991739 0.014362099 -0.005386079 0.018146183 0.012368366 0.006431632
[869] 0.063798936 0.057414183 0.010328949 0.029888270 0.030400181 0.013052428 -0.017734332
[876] 0.006395295 -0.000151400 0.009225683 0.011822913 -0.012261209 0.003756356 0.014860968
[883] -0.029842157 0.004751597 0.018930760 0.058817612 0.019487910 0.006237380 0.002761476
[890] 0.024546540 0.019185811 0.026612414 0.006070896 0.030955961 0.035833762 0.006379187
[897] 0.029716378 -0.006818472 0.009990591 0.005757794 0.025198726 -0.066378357 0.009551091
[904] 0.004527612 0.035425435 -0.019127787 0.042022958 -0.026514777 0.003383995 -0.035409381
[911] 0.029203736 0.015934741 -0.010481638 0.012179697 0.008202882 0.023307838 -0.035591125
[918] 0.029511481 0.056864625 0.008800195 0.014822592 -0.023870149 0.028976823 0.022198034
[925] 0.040705885 -0.047019665 0.033661258 0.013396326 0.028056760 -0.004169823 0.014647351
[932] 0.014213520 -0.015714918 0.046278686 -0.007517957 0.016054498 -0.006509788 0.028289573
[939] -0.044324242 0.024723385 -0.044921375 -0.007520176 -0.009162039 0.014690728 -0.018305795
[946] 0.025137231 -0.001063619 0.008165955 0.023708789 0.000319906 -0.002366817 0.004398441
[953] -0.011777940 -0.002110035 -0.000593120 0.026958964 0.005688029 -0.023181042 -0.004643152
[960] 0.029474770 0.045304698 0.014702044 0.051003017 -0.017537408 0.029679523 -0.005188969
[967] -0.005438543 0.026174842 -0.011049007 -0.007329470 -0.002961007 0.026495510 0.014627785
[974] -0.014271443 0.011850878 0.044880864 -0.017744068 0.010378671 0.009961981 0.028902989
[981] 0.015240378 0.020341042 0.032706815 -0.019006661 -0.007384688 -0.007692411 0.002395278
[988] 0.003319761 0.001336142 -0.033420445 0.002486631 -0.019118270 0.037628086 0.030318822
[995] 0.032768004 0.026228418 -0.007470728 -0.016561743 0.053905805 -0.043760279
[ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,7] #Gram_x_Lex
[1] -0.02643453 -0.01451428 -0.04366503 0.01843834 -0.00076347 0.01209294 -0.01748334 -0.02444153
[9] -0.00861787 -0.00017053 -0.03410152 -0.01743597 -0.02001689 -0.05376000 -0.02712873 -0.06246272
[17] -0.04161960 -0.02835483 -0.05913866 -0.03214733 -0.03096222 -0.02073950 -0.05928627 -0.03070344
[25] -0.04068317 -0.00764850 -0.00514830 -0.02318248 -0.01631486 -0.00948557 -0.03242251 -0.00807094
[33] 0.01616959 -0.02220997 -0.03743642 -0.06746575 -0.01641995 -0.03401093 -0.03212801 -0.00781529
[41] -0.03783019 -0.07492733 -0.01885148 -0.04286303 -0.01136275 -0.05469012 -0.02164856 -0.01797525
[49] -0.01709745 0.01297769 0.00492893 -0.04140623 0.00187186 -0.04469084 -0.05977066 -0.01960863
[57] -0.02182371 -0.02860936 -0.00057314 -0.02651102 -0.03320972 -0.04223014 -0.00491344 -0.01838174
[65] -0.00030431 -0.02783298 0.00552627 -0.02060781 -0.02448125 -0.01608656 -0.03611300 -0.05571256
[73] -0.00834680 -0.01216059 0.02897794 -0.02601901 -0.00534867 -0.05330070 -0.05459600 -0.02066640
[81] 0.00650506 -0.01480814 0.00188709 -0.03217758 -0.01216388 -0.04272111 -0.05887711 -0.00675317
[89] -0.00763020 0.06333080 -0.01137966 -0.01303753 -0.02243008 0.01089048 0.00339523 -0.01322859
[97] -0.02061289 -0.02188776 -0.03326726 -0.00747940 0.00735974 -0.03844854 -0.02681083 -0.00438416
[105] -0.03828524 -0.01361433 -0.02964937 -0.04928472 -0.01449879 -0.03342002 -0.02643976 -0.02073341
[113] -0.00642303 -0.03873478 -0.03927047 -0.02899113 -0.03431393 -0.00122088 -0.01698484 -0.03453561
[121] 0.01178450 -0.02792079 0.00487644 -0.00991496 -0.02067221 -0.02640500 0.03070449 -0.00889728
[129] -0.03004693 -0.00921610 -0.06013195 -0.01617879 0.00019325 -0.06166815 -0.01141566 -0.03581983
[137] -0.04069232 -0.02705157 -0.03256755 -0.02324772 -0.05463163 -0.00037038 0.01894868 -0.01947937
[145] -0.02598582 -0.06407870 -0.02393923 0.00193464 -0.01905001 -0.01622242 -0.00808539 0.00696542
[153] -0.06268436 -0.08193457 -0.04844866 -0.03195551 -0.05555786 -0.01196152 -0.04624227 0.01525784
[161] -0.02704867 -0.02411313 -0.00939314 -0.02052374 -0.01043394 -0.03824097 -0.01875809 -0.07668134
[169] 0.00009467 -0.01897002 -0.01413781 -0.00341205 0.01297243 -0.04184697 0.00166877 -0.03664289
[177] -0.02500279 -0.03159881 -0.03543930 -0.01780031 -0.00779482 -0.02567635 -0.03116231 0.01170112
[185] -0.04849484 -0.06606359 -0.05685330 -0.04721787 -0.06716849 -0.05641866 -0.04178808 -0.04574469
[193] 0.00157066 -0.03630107 -0.00731648 -0.02888703 -0.01615027 0.00050683 0.00577121 -0.02481859
[201] -0.01235304 -0.06007567 -0.02038987 -0.02497667 -0.01118532 -0.02253475 -0.03849516 -0.03194632
[209] -0.03574074 -0.01632911 -0.06890123 0.00820960 -0.01653961 -0.03018833 -0.05682885 -0.01695133
[217] -0.04381806 -0.02098045 -0.03217932 -0.05679859 0.00067117 0.00143422 -0.02893429 -0.02479318
[225] -0.02620596 -0.00840995 -0.00045180 -0.01668423 -0.02240569 -0.04227508 -0.07023026 -0.04341890
[233] 0.00781580 -0.03270959 -0.02349330 -0.00357934 -0.02321941 0.01080959 -0.03787897 -0.00615312
[241] -0.05876903 -0.03614358 -0.00551643 -0.02687662 -0.00128653 -0.03177145 -0.04168294 0.02518072
[249] -0.09282864 -0.02642341 -0.03431145 -0.03934078 0.02870356 -0.05774151 -0.02783546 -0.03569581
[257] -0.07292926 -0.00722331 -0.04090171 -0.01447970 -0.04589897 -0.02387058 -0.03472802 -0.01273755
[265] -0.02319917 0.01342241 -0.04729430 -0.05245226 -0.03079458 -0.06435720 0.02829442 -0.04202410
[273] -0.01359534 -0.01886244 -0.05440801 -0.01384375 -0.02794992 -0.04535853 -0.06550394 -0.02447065
[281] 0.02350221 -0.01145367 -0.06212092 -0.02789046 -0.02609297 -0.01245739 -0.01901068 -0.00791684
[289] -0.02932615 -0.04077011 -0.01799057 -0.04467382 -0.02366046 -0.01558504 -0.04466097 -0.03990633
[297] -0.06722660 -0.00392484 0.00892352 -0.01072640 -0.04850431 -0.02594873 -0.03623498 -0.02226579
[305] -0.01834489 0.03161110 -0.01365192 0.00087102 -0.03940325 0.01287436 -0.03159340 -0.05321306
[313] -0.02273264 -0.03843360 -0.02206083 -0.01899047 -0.02088854 -0.04319470 -0.02249226 -0.04861357
[321] 0.00219132 -0.04203135 -0.03798334 -0.01776994 -0.05160029 -0.05476080 -0.01920159 -0.03703027
[329] -0.02549676 -0.04357840 0.01453175 -0.05182269 -0.00830282 -0.01437660 -0.01006008 -0.01647934
[337] -0.03935809 -0.00697240 -0.01028355 -0.01421691 -0.04752911 0.01034952 -0.00098039 -0.01737055
[345] -0.00807782 -0.00222755 -0.04146218 -0.02481229 -0.04292281 -0.04056561 -0.02537366 -0.04769446
[353] -0.02568001 -0.01215094 -0.02712172 -0.00947555 -0.00800113 -0.05020939 -0.00266159 0.00703621
[361] 0.00850367 -0.02741474 -0.00686226 -0.05661119 -0.02526093 -0.03460484 -0.01700057 -0.05144143
[369] -0.02662308 0.01488023 -0.02894203 -0.01582288 -0.01905348 -0.02322706 -0.02525124 0.00967005
[377] 0.01184858 -0.02068330 0.00312128 -0.00356497 -0.06371916 0.00361304 -0.03003813 -0.06700369
[385] -0.02484764 0.00590362 0.00349995 -0.01083284 -0.01657195 -0.00540002 -0.02771871 -0.03236171
[393] -0.03580009 -0.02339241 -0.00128578 -0.01630400 0.02760939 -0.01304896 -0.01068724 -0.04539129
[401] -0.04127351 -0.03084810 0.00704917 -0.05166535 0.01752555 -0.03369183 -0.04827954 -0.05456975
[409] -0.01116630 -0.04834029 -0.03261413 -0.06028317 -0.03889594 -0.02451126 0.02053563 -0.03972262
[417] -0.04829351 -0.01445773 0.01363454 -0.01482907 -0.03484187 -0.03135330 -0.02342801 -0.00874967
[425] -0.03670503 -0.04178076 -0.04424004 -0.00095267 -0.02989046 -0.02578494 -0.00824375 -0.01316815
[433] -0.05947288 -0.03818290 -0.02474425 0.01076816 -0.03226907 -0.06546323 -0.03620571 -0.02867191
[441] 0.00652991 -0.03456909 0.00060652 -0.00450392 0.00692765 -0.01575644 -0.03356154 -0.03562993
[449] 0.02181551 -0.03599489 -0.00669726 0.02768550 -0.01518058 -0.04376911 -0.03730073 -0.01518469
[457] -0.02897032 -0.02771215 -0.02273453 -0.00221906 -0.03668305 -0.01800505 -0.06041597 -0.06079458
[465] -0.03540480 -0.00816749 -0.05353366 -0.00955651 0.02004832 -0.00539581 -0.04658702 -0.07028570
[473] -0.04270199 -0.02865353 0.04171762 -0.01110867 -0.01461266 -0.04050041 0.01035467 0.00510461
[481] -0.00284029 -0.02296470 -0.01694183 -0.04891627 0.01525564 -0.04652637 -0.05354268 -0.03472809
[489] -0.00682341 -0.02765865 -0.00971251 -0.07216920 -0.00047392 0.00040692 -0.01326636 -0.02496738
[497] 0.00966524 -0.02520933 -0.01923726 -0.02539457 0.01922423 0.00417531 -0.00940627 -0.02461765
[505] -0.00314250 -0.01853697 0.00307019 -0.04976035 -0.00967822 -0.05818942 -0.07288053 -0.06726407
[513] -0.03345950 -0.01911275 -0.00734147 -0.02455671 -0.05006015 -0.03188424 -0.04958020 0.02029221
[521] -0.04139600 -0.03849835 -0.02962409 -0.08544189 -0.05858402 -0.00507427 0.01253951 -0.03943135
[529] -0.02393139 -0.03988454 -0.03128781 0.02543767 -0.00332797 -0.02998702 -0.00369736 -0.01095120
[537] -0.03690548 -0.03988520 -0.01907614 -0.03486920 -0.00848036 -0.02674762 0.00604316 -0.01899584
[545] -0.04451674 -0.04579106 -0.05260233 0.00208135 -0.00642935 -0.02685562 -0.01388928 -0.02246385
[553] -0.03530743 -0.05305907 -0.01627333 -0.02262527 -0.06536191 -0.01303825 0.02371155 -0.01437739
[561] 0.01539837 -0.00257150 -0.02118073 -0.05470416 -0.02971560 -0.04779792 -0.01910332 -0.03562463
[569] 0.01901840 -0.07113551 -0.00045447 -0.03477393 -0.04007794 -0.01873713 -0.00935164 -0.01615365
[577] -0.01949977 -0.02209669 -0.02505321 -0.00021634 -0.02646467 -0.01916797 -0.01256380 -0.03622366
[585] -0.00998133 -0.00967243 -0.05353197 -0.00106051 0.02830872 0.00155172 -0.01486582 -0.05610591
[593] 0.00478220 -0.03555491 -0.00020586 -0.06580379 -0.00736039 -0.01997332 -0.04204674 -0.04269298
[601] -0.00095659 -0.03812233 -0.03950344 -0.01183667 -0.02464647 0.02196897 -0.02767469 -0.05377603
[609] -0.02622700 -0.02589430 -0.04512203 -0.01764576 -0.00881160 -0.03662602 -0.04531826 -0.05111312
[617] 0.00631146 0.00300572 -0.02185265 -0.02993809 -0.04926822 -0.03895486 -0.03158369 0.00484968
[625] -0.04499772 -0.02701169 -0.01764699 -0.04690195 -0.01877269 0.00842860 -0.03228362 -0.03044106
[633] -0.02351278 0.00385739 -0.00772301 -0.02563380 -0.01571325 -0.05910754 -0.01366370 -0.01916929
[641] -0.02695644 -0.01627780 -0.03852938 -0.00745844 -0.04802423 -0.00011451 -0.02839234 -0.00081342
[649] -0.07057136 -0.02281155 -0.04421789 -0.03817229 -0.00913516 0.00622887 -0.05324860 -0.01447360
[657] -0.01896764 -0.02803868 -0.03020571 -0.00626744 -0.00638130 -0.05052821 -0.02530809 -0.05480415
[665] -0.03397229 -0.04305835 -0.03251531 -0.00604614 -0.00763012 -0.01283697 -0.05616155 -0.04416624
[673] -0.02759586 -0.04491070 -0.04162526 -0.02827539 -0.04257298 -0.01517922 -0.02056468 -0.00755024
[681] -0.04257063 -0.00056852 -0.01432991 -0.02506194 0.01542658 -0.06204788 -0.07236074 -0.02578789
[689] -0.06081175 0.00458579 -0.04242465 -0.02385211 -0.05580995 -0.02647968 -0.01393687 -0.03061703
[697] 0.01211544 -0.05127699 -0.02595431 -0.02536342 -0.02325093 -0.00654146 -0.01388877 -0.05333031
[705] -0.04726698 0.00693884 -0.00918853 -0.02739936 -0.01325029 -0.04682944 -0.00957618 -0.03964492
[713] -0.02111677 -0.08008734 -0.04465775 0.00690704 -0.03401665 -0.00908369 -0.00863984 -0.04508560
[721] -0.00978252 -0.01768274 -0.05541814 -0.02901092 0.03097187 -0.05214321 0.00824164 -0.05784582
[729] -0.02645794 -0.01321716 -0.01971365 -0.03832905 -0.04708200 -0.04701990 -0.05158652 -0.02120985
[737] -0.01479490 -0.02843450 -0.02928552 -0.03602659 -0.03268052 -0.05175128 -0.02384001 -0.01491974
[745] -0.02707868 -0.05375941 -0.01425614 -0.02529922 -0.00952068 -0.03097688 -0.01268397 0.00693706
[753] -0.01845683 -0.06163163 -0.07336366 -0.00116291 -0.00130428 -0.03219313 -0.05806433 -0.03281992
[761] 0.01173381 -0.04083586 -0.03575711 -0.04609556 -0.04363068 -0.06080074 -0.00624828 -0.05538195
[769] -0.04386118 -0.04864834 0.06944225 -0.00971917 -0.02259751 -0.02683002 -0.06396183 -0.05945182
[777] -0.04036633 -0.03509833 0.00117676 0.02096622 -0.04094549 -0.01094518 0.00407361 -0.06569276
[785] -0.01375686 -0.01501731 -0.02862459 -0.03735221 -0.01913476 -0.02281421 -0.02460915 -0.03664815
[793] -0.01295285 -0.04024895 -0.05770790 -0.03192626 -0.01738563 -0.00395764 -0.01674623 -0.03427450
[801] -0.02147518 -0.05824836 -0.00375304 -0.02417545 -0.00609911 -0.00340914 0.00416113 -0.01724947
[809] -0.01296240 -0.01114690 -0.01393601 -0.03150531 -0.07093549 -0.03727286 -0.01374972 -0.02140981
[817] -0.01117766 -0.06389543 -0.04256110 -0.01610282 -0.05388490 -0.05037226 0.00373292 -0.03173082
[825] -0.08562668 0.04021089 -0.02799692 -0.02697669 -0.02296435 -0.01205416 -0.01801702 -0.04131215
[833] -0.03191431 -0.02749930 -0.03094314 0.02511232 -0.04878524 0.00203434 -0.02310371 -0.05526603
[841] -0.04098999 -0.01760341 -0.03250831 0.01448453 -0.02583955 -0.06986302 -0.01250631 0.01021258
[849] -0.00541269 -0.01768586 0.02768226 -0.05891068 -0.03225762 -0.03637505 -0.06597700 -0.02366252
[857] -0.04140228 -0.01809750 -0.05058498 -0.01955154 0.00526073 0.00648315 -0.01693454 0.00147817
[865] -0.01773661 -0.06452452 -0.05674149 -0.02629286 -0.05809171 -0.02268760 -0.04812062 -0.04948628
[873] -0.01349378 -0.01541379 -0.02916535 -0.03984560 0.00337029 -0.01147897 -0.03951471 -0.02150401
[881] 0.00049595 -0.02769921 -0.04851395 -0.02324474 -0.03763487 -0.05986193 -0.06646502 -0.00968342
[889] -0.02823357 -0.00290011 -0.00151242 -0.01045226 -0.04004993 -0.03712755 -0.06166138 -0.03269784
[897] -0.05236049 0.01094839 -0.01058729 -0.02307828 -0.03213981 0.00144466 -0.04182505 -0.00654216
[905] -0.04707247 -0.01519781 -0.03501768 0.03061429 -0.01760438 -0.01716675 -0.00923068 0.00382860
[913] 0.01132242 0.01623165 -0.01240650 -0.01025050 -0.01051720 -0.03890299 -0.03175194 -0.01688440
[921] -0.02345485 -0.02446062 -0.03428995 -0.06780624 -0.03653889 -0.01374693 -0.03138218 -0.03982395
[929] -0.00770516 -0.01537408 -0.02318678 -0.02471987 -0.00013384 -0.06435879 -0.02400762 -0.06704189
[937] -0.00987989 -0.03854024 -0.00511353 -0.04120990 -0.02916683 -0.01186123 -0.00029060 -0.00581717
[945] -0.00750516 -0.04196529 -0.03758306 -0.00243677 -0.00811729 -0.03648469 -0.00034186 -0.00590233
[953] 0.01633747 0.02212211 -0.02709491 -0.05319853 -0.04668680 -0.01104105 -0.00191937 -0.04990468
[961] -0.05102453 -0.02932010 -0.04953287 0.00019400 -0.03136559 -0.02214163 -0.01452695 -0.05092710
[969] -0.03485967 -0.00402785 -0.01356430 -0.03125674 -0.05122640 0.03440960 -0.03092051 -0.00782390
[977] -0.02169741 -0.01133890 -0.06916662 -0.03715976 -0.01982440 -0.01790209 -0.05805647 -0.05247393
[985] 0.02229157 0.00520565 -0.01108463 -0.02816843 -0.00829501 0.02204537 0.00010049 -0.01426876
[993] -0.05139977 -0.01943905 -0.03282174 -0.03749179 -0.03978463 0.01728317 -0.07666557 0.07984352
[ reached getOption("max.print") -- omitted 7000 entries ]
# check predicts --> posterior parameter distr. back in normal space
pst_gram <- y_posterior$Gram
pst_gram
[1] 27.10 45.49 43.30 40.76 42.06 41.16 40.56 46.59 48.83 42.73 49.29 42.67 42.40 36.39 46.27 31.79
[17] 33.26 45.11 39.66 44.95 43.86 40.94 30.44 54.60 48.29 45.40 43.71 43.96 41.68 50.19 39.68 28.39
[33] 34.14 35.07 40.66 38.39 42.14 22.94 36.46 19.91 25.91 38.00 38.26 45.78 42.45 23.85 48.72 33.20
[49] 36.39 33.91 35.36 45.11 41.90 40.99 49.50 41.26 21.76 35.23 40.65 30.33 44.89 38.23 44.64 45.45
[65] 34.25 35.27 24.77 43.69 34.73 30.68 32.31 23.00 40.21 36.37 37.36 37.25 46.40 47.31 34.55 40.30
[81] 41.76 56.55 34.33 46.54 38.93 39.30 26.27 39.34 49.40 44.49 43.20 37.51 30.94 32.29 28.35 41.92
[97] 47.50 39.73 22.95 39.83 31.35 45.08 39.90 38.52 29.22 43.10 51.35 34.13 38.94 49.24 31.55 34.41
[113] 44.26 33.22 26.02 27.68 31.09 42.05 30.64 41.87 35.67 35.65 43.66 33.56 34.79 42.43 41.91 36.63
[129] 39.21 35.79 48.94 46.32 35.95 39.94 46.28 39.84 48.18 42.32 25.70 43.10 34.56 47.16 25.78 35.01
[145] 38.30 37.84 26.62 41.54 34.31 31.22 36.05 39.11 37.80 34.28 37.09 32.98 34.48 35.81 42.36 41.81
[161] 40.31 43.58 50.10 42.78 36.47 43.05 31.79 32.19 37.25 35.81 37.95 28.47 31.84 34.05 44.87 32.31
[177] 22.17 31.82 49.55 40.79 29.45 44.37 43.22 54.77 44.58 39.63 54.98 51.28 36.56 45.00 31.27 23.86
[193] 42.13 16.51 47.97 49.88 37.96 47.25 26.66 38.17 39.13 33.27 47.04 48.38 34.10 38.01 35.12 33.49
[209] 48.42 34.06 35.68 39.73 41.27 32.65 44.86 34.13 43.19 39.86 38.70 43.07 41.77 40.60 20.83 30.47
[225] 26.51 43.95 37.73 52.53 45.80 31.70 45.51 31.10 34.87 35.07 49.07 48.18 40.18 48.78 54.11 36.10
[241] 44.87 45.08 32.78 46.13 45.52 38.86 35.96 45.60 38.56 43.87 39.09 44.26 42.74 39.62 48.90 38.13
[257] 40.59 41.14 35.83 37.83 36.02 59.41 40.59 26.56 48.87 57.62 25.46 46.69 27.00 41.20 30.75 52.91
[273] 38.25 32.99 33.07 45.66 28.93 31.58 38.69 34.85 41.54 35.33 32.93 32.62 37.77 32.43 42.99 43.96
[289] 41.65 33.15 29.33 44.22 22.92 46.11 36.85 49.98 28.73 27.56 28.77 40.69 35.78 41.67 40.25 32.63
[305] 44.32 36.53 36.47 42.57 28.04 39.22 38.71 28.35 44.46 56.47 49.18 38.29 36.00 33.77 44.20 28.87
[321] 38.33 20.25 33.74 39.28 44.92 30.41 39.61 30.46 41.88 42.55 44.29 45.76 33.44 29.04 31.13 41.64
[337] 48.94 36.15 35.77 41.46 37.48 37.04 47.27 32.20 16.99 48.08 35.05 44.18 33.89 42.40 43.61 45.75
[353] 41.30 35.38 36.11 31.66 39.46 47.68 37.68 61.45 42.56 41.07 40.97 44.74 32.58 30.29 45.43 45.04
[369] 45.28 29.13 37.06 47.31 38.50 39.27 28.95 38.29 28.38 32.53 32.88 37.10 47.96 36.69 37.43 38.30
[385] 37.65 14.95 36.84 42.16 47.18 44.37 29.24 50.52 60.91 48.74 32.40 55.94 35.24 22.54 31.98 50.68
[401] 36.16 46.99 50.07 65.47 42.31 43.80 54.85 41.02 40.30 44.73 42.10 43.59 44.74 24.89 35.68 46.11
[417] 38.11 33.17 47.22 31.39 42.77 35.61 50.38 38.60 43.78 28.59 41.04 43.72 35.62 41.13 35.14 38.20
[433] 35.67 21.48 41.45 25.02 27.32 21.89 40.99 38.85 45.08 45.38 24.87 54.21 38.51 47.61 26.27 33.55
[449] 37.81 32.45 43.44 36.16 48.37 27.53 26.82 46.29 36.37 46.15 29.24 38.18 38.68 34.07 43.56 34.92
[465] 36.65 37.23 28.58 39.76 34.70 41.93 30.97 49.80 35.70 45.46 36.25 38.60 35.48 46.28 27.16 48.79
[481] 44.76 35.30 49.22 24.38 40.92 24.51 38.34 17.64 38.40 31.29 32.05 49.99 28.95 47.31 36.67 40.93
[497] 48.62 36.35 30.50 23.54 41.65 55.73 40.64 41.72 27.72 29.49 32.45 36.53 33.81 28.57 52.65 42.03
[513] 43.94 56.48 45.29 40.57 40.73 34.75 34.62 43.79 30.52 45.03 29.95 43.24 50.97 25.91 39.40 44.74
[529] 55.56 48.83 39.19 52.78 40.43 35.68 43.54 47.92 39.00 13.46 38.93 36.17 43.45 44.86 40.68 42.40
[545] 33.82 36.32 43.17 50.43 44.77 32.03 51.67 61.58 47.73 38.09 41.48 35.58 31.74 34.11 45.76 50.52
[561] 31.57 42.43 34.31 27.12 43.68 34.47 34.07 47.49 35.66 44.30 41.57 47.56 44.57 45.33 39.81 38.78
[577] 45.16 29.17 49.81 33.69 31.10 33.70 21.47 39.75 40.98 41.29 54.30 46.65 26.27 47.22 41.44 47.06
[593] 39.80 20.43 32.55 38.84 37.60 40.24 34.51 26.43 27.94 29.46 38.60 44.64 48.43 37.27 42.21 37.36
[609] 34.78 32.67 46.53 24.63 40.92 23.19 42.74 42.69 55.94 45.20 42.93 37.34 21.24 48.06 38.81 36.74
[625] 28.50 34.66 51.64 24.11 42.70 33.69 49.61 31.12 35.94 42.95 18.01 41.22 36.51 22.29 37.28 25.30
[641] 34.58 33.80 38.33 45.29 35.00 39.46 37.98 19.67 47.55 34.04 38.66 45.94 34.64 36.24 56.24 38.48
[657] 54.77 42.68 39.54 50.82 25.67 31.31 38.02 35.85 37.64 35.74 43.49 46.70 28.46 20.87 28.36 37.71
[673] 49.02 42.97 38.95 35.06 38.55 42.40 46.68 45.46 42.76 38.94 33.30 47.20 27.99 36.12 38.43 11.95
[689] 39.01 30.01 55.49 41.69 40.52 42.66 40.36 37.47 20.48 32.64 42.45 44.04 33.86 41.07 45.17 39.06
[705] 43.55 37.45 50.01 37.62 35.11 28.81 49.75 33.99 37.93 28.95 41.33 37.26 36.87 34.66 27.85 44.85
[721] 36.13 38.09 30.13 43.01 33.32 41.97 50.49 27.40 24.47 57.25 34.06 41.25 46.13 41.11 31.00 42.54
[737] 46.76 46.99 38.37 42.79 38.55 49.12 30.07 47.29 48.66 43.13 45.55 50.96 39.54 39.35 38.00 31.27
[753] 37.19 39.71 35.44 35.33 39.82 32.21 40.59 33.77 48.85 39.61 37.05 33.57 36.57 39.76 48.75 38.77
[769] 36.73 38.05 39.74 39.14 37.44 43.66 31.98 28.20 38.18 43.10 38.99 32.14 37.49 28.45 33.25 42.86
[785] 42.48 43.77 42.55 58.05 39.02 37.26 33.41 31.23 23.13 23.93 42.19 33.80 44.15 40.78 32.70 45.06
[801] 30.47 50.78 46.63 32.26 46.39 42.67 39.61 37.64 49.40 37.94 38.38 47.66 44.20 46.64 40.47 21.86
[817] 43.07 54.32 28.37 41.27 30.87 34.73 33.74 37.88 25.36 31.53 32.07 41.67 56.06 32.64 31.10 38.96
[833] 50.25 41.98 44.30 45.42 27.49 38.47 41.56 28.39 26.27 29.15 38.43 33.03 38.22 35.87 36.67 49.87
[849] 42.01 31.27 42.21 41.24 34.41 44.08 48.98 29.80 35.10 41.01 44.10 31.83 34.56 30.55 52.72 42.29
[865] 44.19 38.25 42.14 37.44 46.68 41.87 46.61 25.78 46.55 46.32 31.56 31.70 45.55 33.13 25.00 45.13
[881] 37.40 40.00 34.85 42.49 36.44 39.96 40.29 53.42 30.18 33.54 44.20 20.98 45.28 52.57 43.36 33.53
[897] 45.77 29.51 50.31 36.12 30.79 31.57 43.87 39.79 33.53 40.33 34.81 22.62 39.85 42.30 43.57 23.37
[913] 39.81 43.55 49.34 35.04 37.66 40.91 33.46 35.04 49.24 34.46 53.34 48.62 44.94 29.69 23.52 49.73
[929] 23.62 36.56 42.11 46.46 45.90 32.35 30.47 42.73 49.06 50.56 20.70 53.77 28.34 28.86 32.33 41.39
[945] 46.06 41.84 29.00 42.23 40.92 36.82 43.34 46.56 44.96 31.56 30.28 41.69 41.21 41.71 52.94 42.60
[961] 46.08 34.73 40.12 32.32 42.53 26.66 46.65 38.65 36.72 52.23 41.33 46.19 29.88 30.67 24.31 39.51
[977] 19.77 48.66 34.62 38.46 41.11 30.98 46.41 37.11 37.51 52.23 20.03 33.07 41.52 56.64 21.57 47.38
[993] 33.74 52.68 27.14 18.89 44.79 54.51 38.42 42.86
[ reached getOption("max.print") -- omitted 7000 entries ]
density_gram <- density(pst_gram)
plot(density_gram, main = "Density Plot of pst_gram", xlab = "pst_gram values", ylab = "Density", col = "red")
pst_gen <- y_posterior$Gen
pst_gen
[1] -0.336259 5.421016 -2.070452 -3.864327 -2.329377 9.599332 2.341612 3.888579 0.195945
[10] -0.988255 -10.482393 4.726151 -10.735708 4.812651 0.613476 -15.421126 7.458396 -4.367548
[19] 3.511281 7.294645 9.841403 9.990754 -1.180291 -3.691716 -9.741715 -6.627670 -5.137832
[28] -0.848989 -10.407494 -6.197579 -15.935158 -5.605109 -9.054800 0.987422 -3.819461 -3.991178
[37] -7.966152 -5.786592 -0.008239 -10.442185 -5.798388 2.378973 -6.005132 -5.070985 -10.575037
[46] -10.217830 -13.407330 -16.773603 -2.616412 9.115410 -4.355538 -10.593461 0.872785 -13.431707
[55] 8.038806 8.276597 -3.279344 -5.991877 -4.478842 -9.741289 20.220092 -13.947706 0.058628
[64] 5.310283 7.230607 -7.122810 -8.723101 -10.102857 -9.986747 -14.460826 5.285884 -5.258822
[73] -23.001661 -0.110396 -0.230776 4.044957 4.845807 7.577506 -10.588604 2.924012 -9.978517
[82] -0.611442 -10.188708 -11.057308 -2.218713 -11.259309 -4.327092 -6.016482 -7.908411 6.043780
[91] -3.457963 -11.819914 -6.936388 -8.807306 -13.852171 -2.320043 -8.308554 -4.839289 -3.626115
[100] -5.470007 -9.724511 6.483118 -9.606888 5.468177 2.192224 -2.272653 1.146615 -8.998758
[109] -9.547409 -1.648923 -10.509915 -0.867274 4.734686 -5.956585 -10.486839 -8.223798 -5.317020
[118] -1.761636 -2.197854 -14.496968 1.895192 -3.609850 -0.691089 -1.329228 1.859751 -0.850321
[127] 3.082366 -4.601353 -5.486166 3.466837 5.326725 4.752155 -5.161996 6.136557 -4.138717
[136] 2.902578 -14.815336 3.719221 -1.080444 1.615780 -0.756766 -9.213037 -9.905852 1.042570
[145] -2.194072 -4.470309 -0.384314 2.931838 -7.280097 -9.708149 -16.727450 3.891873 -0.391361
[154] -12.215520 9.142799 1.675985 0.536396 -9.714720 -7.990414 -9.127843 -3.971838 6.090579
[163] -6.049193 2.922225 0.985855 -3.408628 -9.722008 -2.318991 -4.309190 -1.024400 1.532153
[172] -14.351198 7.219340 2.448024 -8.178926 -8.942536 -15.651781 -7.239871 -8.041255 -10.989651
[181] -5.221224 -4.209503 -9.244022 8.657323 -13.747497 -3.722136 -3.460394 3.092181 -2.266351
[190] 11.052833 1.861565 9.756665 2.954138 0.015717 11.221877 -3.376741 -8.858695 -12.884660
[199] -6.217670 -1.183323 0.992734 1.752425 -10.377984 0.092506 -3.936242 19.095561 2.547117
[208] -3.200126 -11.509861 -8.259332 8.138252 1.107799 -0.062468 7.605979 -2.878756 1.674620
[217] -6.951288 1.807621 7.247213 6.302034 -14.518420 -7.739977 -7.133773 -7.593429 -6.854018
[226] -6.878293 -7.157974 -1.858156 -5.613795 -10.181565 -5.897846 6.099591 -8.374010 -12.892262
[235] 6.981454 -6.266595 -5.178673 -16.330671 -9.065957 -18.787914 -6.725170 -11.151132 -5.366024
[244] 5.917880 13.226704 4.898431 3.104295 -0.733569 -13.392922 -0.361732 -3.648410 -7.267860
[253] 12.704164 -13.308319 -5.315701 -4.833460 0.499770 -12.044276 -4.927664 21.041837 -2.122881
[262] 1.513560 -2.438550 2.510813 -3.732281 6.017305 -3.431176 11.495216 5.138604 -13.016560
[271] 0.085832 0.011009 -3.057723 -17.943717 -0.865185 -2.433680 3.844409 -5.962113 0.562913
[280] -1.689453 6.389848 0.472891 -8.526555 -19.406529 10.686556 -3.178385 8.536322 3.091768
[289] -9.508803 -2.913570 -1.051159 6.647423 0.644260 2.490714 4.889970 -10.632756 -7.185918
[298] -8.321120 1.132598 -12.573792 -9.430872 -9.413185 2.038796 -2.418698 7.177597 -5.380919
[307] -4.994889 -8.003778 -8.113601 -9.660847 -6.552602 -3.885869 5.574066 -5.838091 -0.835261
[316] 5.071017 -0.426455 -0.816985 -7.374792 -3.641848 -13.805133 3.906461 -1.748363 -11.278552
[325] -1.336404 2.247451 -4.762906 -9.126644 -3.003583 -2.531765 -11.627186 -10.586453 -12.351023
[334] -6.310643 -1.544622 -8.859113 2.006791 -6.782818 -3.922657 -10.060834 -10.288379 -5.108049
[343] -5.556675 -2.728294 -0.691989 -7.404462 1.461574 -15.486168 -4.040345 1.275892 -4.839467
[352] 5.582496 1.870991 -10.945154 6.035028 6.615273 0.033980 -1.712664 5.928935 -6.709352
[361] -5.013635 -5.460083 -15.548563 0.643574 11.822351 0.592219 -6.085920 3.631806 -13.145286
[370] 1.066228 -3.605192 -9.138485 12.505291 -10.529564 -3.897962 -14.133241 7.829986 -2.043486
[379] -1.568830 -0.140820 -7.918661 -16.303969 2.086900 -5.008017 -4.762888 -3.205484 11.430613
[388] 3.350996 -5.672120 -2.282673 2.577225 7.769017 -6.739366 -6.693238 0.240406 -2.745810
[397] 5.525949 -6.204752 2.753149 8.897074 0.054514 -5.123753 -5.316051 -7.852384 5.046129
[406] -0.349686 -0.089750 -7.706524 -10.840908 -7.127448 -0.724264 -9.376372 2.825296 -14.257384
[415] -4.026594 6.668552 2.101860 -9.356290 -8.883704 -7.734120 1.441323 -16.421712 -1.519138
[424] -18.770932 -10.279811 0.309338 -15.683323 -5.870601 -0.089618 -8.222879 -11.994334 -9.402575
[433] -6.290123 -5.889831 9.221920 2.329413 10.664019 5.435983 -7.626526 2.330151 -3.417962
[442] 13.386851 -2.737955 -3.633300 0.082573 -9.859663 3.344967 -3.964731 -10.367991 3.167621
[451] -4.510495 0.930472 -3.558075 -2.059570 -3.020554 10.767542 2.904050 5.861457 -4.422559
[460] -5.461730 -6.421791 -8.800073 -3.533588 -7.136701 -7.678167 4.777818 -5.220223 -15.172452
[469] -5.082663 -13.866035 -3.002074 -9.838850 -12.721553 -16.420930 -5.145851 -18.427130 -12.064579
[478] -4.937791 -4.413706 1.185228 -15.474649 -11.623900 -5.772791 -3.211692 -10.425600 0.637551
[487] -1.093399 -8.043331 4.159968 -12.963382 -9.302864 0.693651 -10.580614 6.308634 2.602516
[496] 5.780803 -5.878647 -4.778002 -11.124135 0.490276 0.635072 -18.268293 -7.397223 -2.213956
[505] -10.404636 6.548354 -0.663647 3.099890 -10.524805 -3.044989 -1.673535 -10.526193 -12.206890
[514] -3.959823 -3.224963 -12.303659 -4.882121 11.791215 -4.246273 -8.087787 0.311195 -1.793512
[523] -5.257716 -1.578111 10.526340 7.124213 -6.867733 -7.909121 -5.243312 4.636888 4.015280
[532] -5.189137 -9.769807 -1.886358 0.318543 1.753459 -3.440635 3.242442 -7.207645 3.891741
[541] -10.241986 -1.300800 -2.123658 -8.561048 3.956822 10.063113 -11.863912 -1.988541 2.217851
[550] -11.643598 0.947872 2.377501 -2.072088 -16.979405 4.237909 -6.248691 -4.908682 -11.274428
[559] -11.109068 -10.247177 -10.807653 -12.084922 -1.860680 -7.814641 1.583978 -6.294620 -3.704085
[568] -2.677218 -4.712909 -11.650945 5.762367 -11.653761 -3.170230 -7.505287 -6.379574 -5.873133
[577] -9.605467 13.053028 11.248985 -1.943644 2.665236 -2.815999 -5.433507 -1.815993 1.496122
[586] 14.782780 -6.448585 3.101977 2.948170 0.652649 -10.732502 -8.039938 -16.897107 -10.051772
[595] -17.735048 -2.720669 -3.265224 1.878355 2.133142 -0.192888 -8.664137 -4.712270 -6.522238
[604] -7.035999 1.522853 -4.367934 -0.709598 -8.265768 -0.959071 -7.124068 -1.374159 2.677996
[613] -0.084338 -0.897388 -0.423820 5.044638 -2.504798 -6.926689 -9.853938 15.441779 0.559376
[622] -5.787111 -4.611835 -1.949169 -4.244757 -9.307143 2.895162 -7.232328 -2.817908 -18.422490
[631] 6.954249 4.850912 2.228307 -2.762239 -2.224826 -9.025058 2.874764 -0.587615 0.677173
[640] 0.252267 -6.381958 0.298355 -3.498125 -11.145297 -16.624314 0.223214 -6.799687 -8.600359
[649] 4.232203 -0.369426 -0.012786 4.052832 3.373167 -2.427460 -5.169458 22.240377 -14.338425
[658] -7.484807 -8.888818 -2.149348 5.686771 6.222934 4.292096 -8.156881 -4.905346 -8.174250
[667] -11.235034 -7.489959 0.673323 0.699940 -6.228764 3.349266 -6.726206 -1.518387 10.887462
[676] 3.733590 -2.190056 11.482970 -6.392277 -7.853837 12.742159 4.848498 -14.188902 18.887564
[685] 4.626860 2.001671 -4.638811 -2.880858 -3.037511 -1.519940 -12.281872 -0.321047 2.471579
[694] 0.252273 7.127941 -7.363657 -2.699759 0.033462 0.211019 -0.933889 -4.583647 -11.602200
[703] -6.246194 12.946906 -4.279646 4.150455 -8.373680 -18.110741 -2.275151 -10.871130 2.536159
[712] -6.897010 -3.709795 -10.371767 -4.353305 -9.372623 -3.328026 7.562994 -18.352838 -3.725053
[721] -5.894845 -6.070735 -6.866515 5.597946 3.541493 -15.277935 -0.714392 4.517807 -15.756803
[730] -5.332781 -5.738092 7.477545 5.842889 -14.668083 -10.507044 0.921243 -5.171982 -14.365022
[739] -4.512105 -4.597606 -3.458893 2.230619 -15.313013 3.457920 3.877492 -2.579793 -10.236007
[748] -4.312396 1.315565 -2.509448 -5.823834 -3.789558 5.658204 -3.740385 -9.994625 0.289183
[757] -18.198976 -2.117052 0.379055 -10.760162 -2.642569 8.541824 -7.981574 -6.642612 -11.236099
[766] -3.030748 4.340423 6.854069 6.880042 -7.588970 -1.450887 -3.323773 -2.033044 -11.896962
[775] -12.137599 -2.185407 11.527898 -1.990899 -1.149890 -6.607528 8.949967 -5.322564 2.025328
[784] -5.924485 -12.547526 4.356636 -3.206642 -12.668430 -4.482629 -10.962337 -9.015587 -6.396233
[793] 0.649867 -5.341951 -0.544155 -3.588094 -1.569619 -8.391832 -11.064584 4.296106 -11.442264
[802] -10.375211 -1.583640 0.513151 -10.992682 -5.933961 -9.307166 -8.249038 -6.164651 -2.210997
[811] -2.529593 4.410264 -27.331451 -10.632110 -0.626608 3.339534 1.238447 -2.256733 -27.693039
[820] -1.748364 0.490824 2.245630 -2.153223 0.657139 -6.640139 1.656372 -0.351088 -11.242928
[829] -0.548513 -5.275882 -4.585528 -4.674580 -5.336262 -3.802008 -6.706245 -14.855536 -11.439570
[838] 10.909850 -0.489173 -9.429571 -6.930507 4.036630 -20.486067 -21.252817 -8.012113 -1.781713
[847] -14.819506 3.731105 -2.562823 -0.711222 4.877506 -2.908695 -4.808512 -0.304009 -3.662441
[856] 1.504528 -11.888605 -13.701576 0.945294 -2.642560 -1.183030 -9.609107 -10.631757 -0.369818
[865] 10.928827 -6.035046 -7.571440 -5.871314 -12.546406 -3.327989 -11.370841 -0.262228 -7.066307
[874] -0.782537 -9.715518 -2.098000 -0.886800 4.450389 -3.577947 -0.573379 -1.845207 2.862648
[883] -7.543040 -5.304020 -16.416821 -7.116444 -4.882347 -2.202597 5.437394 -0.669122 -10.578645
[892] 3.686107 -13.922494 1.335694 -13.964651 -5.439115 -8.585105 -1.833396 7.282172 -9.805231
[901] -9.498753 -1.199273 -1.495989 -1.276519 14.819276 -10.985668 -1.958360 -1.087211 1.644404
[910] -4.154147 5.349412 1.357512 -10.704006 -3.254556 -6.822688 -4.190733 -2.175990 -2.356131
[919] 3.305140 -6.217610 -2.479293 -1.604580 9.682701 -6.927220 1.277452 5.872328 9.717050
[928] 1.411570 3.655255 -4.242744 -22.034471 -0.834294 -3.205084 -7.545228 -7.526234 -15.037676
[937] 7.715554 9.053072 5.487070 -1.913270 -8.318529 -8.327044 -2.076590 1.458385 -0.208224
[946] -6.297460 -16.019301 -14.842226 5.339955 -5.230499 -8.618249 -11.922304 -8.313197 -4.995109
[955] -10.273642 -5.026583 -10.264537 0.415520 -0.005511 -6.925420 -9.498108 -12.780217 5.950772
[964] 2.694525 -9.317935 2.075808 -2.486301 -11.055316 -8.006491 -13.613982 -14.305346 2.703369
[973] -2.779209 -2.589081 -1.827941 0.616089 -2.297181 -2.177360 13.976722 4.708046 -2.939251
[982] -2.878822 6.233753 2.406556 -8.536830 -10.289464 0.601656 -3.159437 -3.099671 -11.047786
[991] -6.438881 3.012653 -12.948514 -4.206152 -8.424725 3.987734 -3.897745 -8.679267 -3.194952
[1000] -6.259942
[ reached getOption("max.print") -- omitted 7000 entries ]
density_gen <- density(pst_gen)
plot(density_gen, main = "Density Plot of pst_gen", xlab = "pst_gen values", ylab = "Density", col = "red")
pst_lex <- y_posterior$Lex
pst_lex
[1] -28.9784 -48.8464 -48.6659 -74.4814 -39.9429 -48.3171 -57.9452 -53.0551 -67.9843 -32.7947 -48.0880
[12] -22.1473 -4.6382 -54.1656 -54.2418 -34.8009 -49.1298 -40.7403 -58.0540 -39.4102 -35.4156 -29.7107
[23] -43.0456 -33.3631 -59.1739 -55.3473 -63.8869 -56.3093 -52.3142 -44.8831 -31.8300 -42.8867 -39.5746
[34] -60.0639 -42.5531 -26.0382 -53.7627 -8.1613 -24.3729 -17.3964 -23.3687 -42.9663 -61.8065 -50.8497
[45] -40.9928 -31.5663 -57.0513 -64.9309 -68.7596 -31.3802 -16.3857 -47.7228 -62.1695 -36.2341 -82.0391
[56] -61.0224 -55.4615 -59.5408 -45.8443 -42.4482 -54.7654 -56.8329 -40.4590 -60.8457 -46.8965 -22.3780
[67] -51.5869 -75.8099 -52.4062 -74.0965 -52.2400 -49.0775 -58.2055 -18.7304 -58.0830 -60.0659 -70.7579
[78] -53.3362 -48.2014 -30.7423 -70.5273 -73.0290 -45.0579 -52.5530 -39.0295 -40.3220 -6.6425 -50.4010
[89] -42.1585 -66.1515 -31.5218 -57.1437 -52.6457 -32.3696 -77.8307 -43.2744 -56.5244 -47.7086 -50.9040
[100] -52.5079 -56.0727 -55.3685 -26.1110 -31.4530 -37.6039 -35.7349 -61.6095 -49.8232 -45.7617 -52.9481
[111] -65.0072 -42.4583 -34.4074 -51.7705 -32.6369 -46.1656 -22.5146 -37.4077 -27.0119 -60.8437 -68.5764
[122] -36.8392 -34.5667 -54.1139 -47.7997 -60.6075 -46.1492 -53.2043 -29.9170 -23.4286 -50.5434 -26.3755
[133] -35.7918 -64.6998 -39.3265 -37.5534 -22.5184 -46.8131 -38.1172 -65.2626 -18.0268 -18.3047 -48.1816
[144] -51.7322 -22.7189 -47.9134 -46.4580 -62.2738 -51.1022 -41.1968 -57.0066 -19.3754 -60.8222 -63.8411
[155] -27.2622 -50.8919 -28.5352 -75.3474 -55.6171 -61.7040 -17.9785 -32.2734 -55.1498 -40.7742 -49.0225
[166] -56.4983 -54.2088 2.7606 -62.0358 -57.5506 -37.1763 -39.2469 -72.0128 -50.7302 -43.9567 -55.5483
[177] -61.5541 -23.8100 -71.1487 -47.7458 -59.7675 -99.4391 -47.5164 -43.1114 -30.9776 -69.1521 -44.8970
[188] -38.6891 -43.7506 -56.5714 -50.0970 -23.8125 -25.7381 -63.9534 -43.8607 -31.3135 -62.2373 -42.6637
[199] -32.3645 -26.5889 -47.2981 -27.0559 -35.0705 -54.1000 -30.7968 -74.6078 -55.3254 -36.5379 -50.4122
[210] -40.4320 -53.9291 -60.3227 -62.2652 -38.0795 -73.6476 -54.3904 -16.4484 -34.0917 -58.2071 -58.1659
[221] -27.6025 -48.6308 -27.5435 -41.1779 -31.6309 -21.9580 -63.5180 -15.2432 -47.6551 -50.9665 -50.4306
[232] -72.3619 -67.7943 -44.8980 -42.2692 -39.6455 -59.9869 -21.3759 -35.1168 -63.4726 -69.6429 -34.3614
[243] -36.3644 -34.3067 -48.2227 -57.8380 -56.9317 -48.9925 -3.3359 -39.9114 -48.1312 -56.2780 -18.3854
[254] -37.7972 -29.6002 -42.3780 -13.2385 -78.4219 -38.0517 -92.2381 -13.8458 -65.5922 -42.0447 -27.4635
[265] -41.4781 -78.2570 -70.8704 -42.9243 -54.8011 -68.3926 -54.2189 -66.4858 -35.9515 -62.4033 -38.8859
[276] -37.6137 -41.6559 -40.9528 -56.0782 -46.9772 -25.2767 -47.2714 -20.4864 -4.5802 -36.1391 -59.1391
[287] -46.5341 -45.8537 -63.5543 -63.1922 -64.0906 -72.6739 -32.0521 -36.9963 -17.2504 -39.7410 -31.7366
[298] -70.8326 -64.9302 -42.4879 -39.5246 -38.7591 -54.8130 -45.8022 -62.7265 -35.3236 -40.8772 -56.8053
[309] -28.4380 -45.9468 -69.6221 -27.0037 -45.4053 -52.8309 -47.6559 -39.8191 -53.7140 -57.9904 -83.9634
[320] -37.2964 -63.0051 -42.1295 -77.4016 -42.1952 -59.0143 -47.9888 -43.2383 -45.1163 -32.4543 -23.3728
[331] -59.7675 -41.3630 -52.8709 -43.6518 -32.7486 -50.9338 -51.4025 -48.9997 -46.8638 -32.0118 -34.5111
[342] -41.3383 -44.1903 -44.5899 -16.4941 -35.4117 -33.2140 -52.1811 -61.5815 -32.5321 -41.7927 -50.1412
[353] -38.2818 -39.0862 -60.9361 -31.8912 -67.8725 2.5573 -43.4386 -50.8297 -25.2769 -78.9892 -37.6562
[364] -28.6232 -45.4607 -53.2057 -46.9439 -60.8771 -40.1829 -32.4409 -23.2265 -45.7180 -58.4582 -36.9545
[375] -50.2705 -16.3533 -57.5844 -40.0986 -37.2508 -26.5117 -29.0828 -61.2382 -48.2142 -28.8898 -64.6334
[386] -40.2095 -22.2026 -47.2689 -49.3410 -61.5622 -55.9906 -20.2921 -66.0966 -47.5200 -45.6359 -55.9871
[397] -40.6498 -79.9705 -48.7511 -56.8398 -56.8929 -48.3449 -56.3779 -11.3221 -55.5884 -47.5442 -46.1509
[408] -40.9607 -54.0206 -69.7419 -44.0592 -45.5884 -41.5302 -59.3424 -17.5159 -16.3349 -61.3657 -44.4908
[419] -31.3686 -47.7015 -72.5286 -19.5696 -71.5192 -45.4764 -33.1640 -60.1328 -33.8915 -53.8983 -45.9301
[430] -20.4616 -62.0677 -33.9923 -52.4632 -51.6153 -81.0496 -58.6994 -25.5493 -27.2170 -58.1879 -48.8188
[441] -27.4844 -66.6259 -76.3550 -61.6366 -58.4629 -59.5681 -63.7940 -78.3381 -46.3427 -43.0267 -57.8263
[452] -35.1177 -36.2780 -43.0006 -39.9058 -53.0689 -52.4827 -57.0942 -41.7765 -49.2382 -6.5016 -28.2714
[463] -56.2775 -65.9396 -76.2089 -52.8770 -38.3796 -23.8384 -9.2932 -51.6291 -43.7374 -37.7674 -15.8919
[474] -40.7552 -57.4728 -40.9373 -47.3183 -27.1886 -67.2576 -59.2804 -56.3030 -43.0716 -43.5776 -26.7201
[485] -31.0644 -55.8398 -24.3682 -53.7986 -52.9868 -25.4133 -48.9819 -56.8035 -70.4822 -54.2384 -67.5527
[496] -63.8996 -38.6424 -42.5835 -33.7094 -66.0069 -58.6579 -20.0931 -60.2014 -47.8324 -75.5950 -52.8223
[507] -48.3168 -33.6582 -50.5841 -36.6467 -83.6442 -60.7052 -64.2673 -59.1637 -35.4241 -53.0595 -61.0668
[518] -55.6317 -75.1830 -70.2986 -40.1128 -29.9268 -48.7326 -10.0446 -58.1771 -52.2003 -46.9433 -29.7474
[529] -43.0970 -81.6457 -42.3257 -38.2701 -58.6381 -38.2115 -38.7622 -63.6869 -65.7102 -31.5055 -41.3869
[540] -79.8349 -7.7417 -56.0968 -41.0272 -41.5611 -57.7897 -72.6570 -49.1237 -39.5166 -55.7729 -33.1530
[551] -78.9948 -10.3650 -50.6461 -66.7662 -31.3740 -49.1929 -53.4004 -43.9548 -65.1506 -42.9533 -53.6842
[562] -56.3222 -27.8292 -38.8316 -42.6855 -37.4694 -47.9717 -60.1875 -41.0529 -16.7713 -33.0195 -24.7517
[573] -41.7432 -75.4658 -72.3419 -61.5613 -63.9681 -40.4091 -31.3154 -56.7771 -48.8843 -56.1070 -65.9857
[584] -27.4552 -74.0113 -21.2363 -31.9419 -51.8302 -52.5924 -45.9460 -49.8789 -53.7730 -23.9816 -25.5993
[595] -44.8476 -76.2606 -53.0117 -9.1730 -19.2331 -33.4916 -32.8716 1.5991 -69.3964 -22.3104 -49.8355
[606] -63.4589 -33.5658 -73.5012 -23.2802 -43.0454 -31.2704 -22.6395 -46.6249 -47.5839 -60.5269 -34.4997
[617] -57.3566 -63.8527 -42.7551 -20.1365 -47.0733 -18.1632 -81.5471 -42.7499 -52.3330 -47.2908 -51.7209
[628] -58.3883 -53.2710 -23.8522 -40.7875 -39.6735 -58.2886 -61.5118 -50.5434 -26.1583 -51.7632 -57.1068
[639] -29.2980 -62.8619 -26.5933 -56.1771 -31.5166 -58.1049 -50.3175 -58.3218 -64.8691 -52.9069 -55.6022
[650] -12.2732 -79.2374 -43.6154 -38.1620 -53.9810 -33.2270 -62.2463 -40.8734 -29.7133 -27.4666 -42.5192
[661] -57.5553 -57.7248 -39.2309 -65.7226 -64.1557 -66.5807 -60.5698 -49.9388 -50.5718 -20.6438 -46.0346
[672] -50.6823 -41.9241 -43.4991 -50.2306 -38.8378 -60.8491 -42.8522 -31.4606 -18.6048 -60.0479 -49.4672
[683] -46.6660 -97.5817 -19.1757 -33.9994 -82.1649 -53.3446 -37.6726 -12.4544 -55.0835 -37.0400 -24.0872
[694] -35.4847 -73.3366 -30.6902 -36.1618 -46.5191 -76.3363 -15.7446 -45.2504 -51.6229 -86.8014 -33.9769
[705] -66.9848 -25.3010 -64.4380 -46.1889 -47.1887 -52.2388 -32.0120 -46.7049 -21.6026 -58.1921 -37.0749
[716] -43.7320 0.3159 -26.5868 -21.7685 -51.9372 -22.6049 -29.9407 -36.3897 -25.7312 -50.4457 -35.6543
[727] -40.9632 -43.5020 -10.2513 -52.8279 -12.7225 -43.1980 -19.4441 -40.7771 -27.1007 -36.8641 -43.5199
[738] -12.4249 -18.9560 -31.3255 -52.2504 -44.4392 -62.1820 -52.0239 -71.4287 -24.2586 -33.8851 -63.2818
[749] -82.2694 -45.3079 -23.3593 -56.8680 -38.9808 -60.5030 -54.7535 -54.5708 -60.1583 -68.4213 -43.5838
[760] -57.2149 -52.6049 -58.4670 -45.7643 -44.0127 -38.0033 -56.6568 -33.4472 -44.9987 -43.1393 -37.3633
[771] -35.1194 -32.1802 -73.9632 -19.5728 -38.4634 -24.0586 -71.3746 -37.4011 -61.6437 -36.0926 -61.8039
[782] -29.5818 -40.0871 -39.2438 -86.5686 -50.1873 -57.0894 -46.1400 -33.1190 -61.9365 -54.7465 -7.7994
[793] -59.8099 -47.0245 -47.3099 -34.7706 -38.1221 -26.3219 -37.4225 -46.6122 -27.1250 -66.2748 -20.1099
[804] -53.9471 -66.4616 -53.8838 -44.8390 -52.1157 -20.0068 -37.2206 -49.1530 -29.7206 -36.6539 -70.9588
[815] -66.7235 -47.8615 -28.3909 -45.7743 -27.9220 -54.7617 -54.6988 -47.5585 -54.4603 -36.2785 -42.1418
[826] -34.9467 -16.3053 -11.0089 -58.7069 -48.9455 -56.6498 -54.7384 -28.6062 -18.1325 -66.4964 -20.5924
[837] -38.7652 -57.5220 -20.4264 -36.1317 -57.5981 -4.4110 -50.6138 -37.8860 -45.6221 -59.0096 -74.1251
[848] -42.7322 -28.2268 -46.4383 -54.8069 -42.4450 -57.4603 -28.3973 -43.0943 -38.0123 -17.6828 -52.1603
[859] -35.2787 -33.8872 -8.5433 -54.4864 -71.6543 -23.4217 -70.8560 -86.8485 -41.5390 -11.5702 -43.2081
[870] -55.2893 -54.7594 -63.8185 -46.6554 -74.0936 -56.8596 -26.1396 -60.2982 -13.4989 -20.9027 -11.8357
[881] -50.2213 -55.3923 -30.4494 -49.7939 -40.7392 -67.2094 -57.2483 -54.2784 -27.1308 -31.5282 -62.6907
[892] -47.1592 -34.6357 -42.4748 -48.7905 -50.8117 -59.9573 -44.7202 -69.8463 -14.3379 -20.0656 -52.2570
[903] -22.6820 -50.8503 -55.8745 -51.2371 -52.4351 -47.4203 -36.1433 -53.5402 -68.0331 -7.3697 -55.3364
[914] -62.7637 -40.5478 -24.6937 -43.7957 -20.0956 -46.6609 -30.5675 -31.5002 -38.3047 -48.0107 -29.9340
[925] -51.6090 -18.0960 -48.0767 -29.3699 -4.7991 -52.5382 -44.3430 -12.4570 -68.6330 -39.7352 -17.7631
[936] -30.1121 -31.5038 -37.0354 -53.0928 -60.2398 -30.5177 -51.5079 -59.1942 -55.4052 -33.8782 -47.8367
[947] -44.9996 -40.1110 -73.9918 -54.4412 -30.5024 -55.0213 -43.3797 -32.8785 -39.3628 -40.4416 -44.0316
[958] -39.9926 -45.7505 -37.7986 -34.4473 -49.7366 -59.0971 -53.8776 11.7043 -38.7647 -70.9813 -35.0141
[969] -47.1937 -69.3786 -63.9308 -54.3442 -13.5827 -62.9035 -39.6561 -66.6570 -57.8323 -46.6185 -38.6158
[980] 5.9377 -57.3744 -34.3530 -56.9128 -30.2741 -56.4504 -44.4584 -72.5113 -46.7703 -45.5445 -39.4245
[991] -57.8440 -78.5168 -58.1716 -40.6264 -38.0933 -18.5861 -47.8490 -49.2881 -58.1686 -56.7038
[ reached getOption("max.print") -- omitted 7000 entries ]
density_lex <- density(pst_lex)
plot(density_lex, main = "Density Plot of pst_lex", xlab = "pst_lex values", ylab = "Density", col = "red")
pst_synt <- y_posterior$Synt
pst_synt
[1] -24.35131 -52.69917 -20.76281 8.89170 -10.47833 -39.53073 -14.65562 -30.80821 -45.18107 -45.43449
[11] -43.02262 -12.68632 -19.43547 21.75472 -24.52983 -28.34679 -13.36468 -32.29432 8.43637 -24.73748
[21] -52.88441 4.53085 -25.96000 -32.17645 15.44621 -15.26178 -36.06610 -5.59934 -30.94408 -31.87567
[31] -6.97469 -12.50490 -18.28703 -10.31003 -9.49701 -43.87841 -26.03715 -27.71712 -62.60267 -49.71388
[41] -41.57826 -7.32022 -12.09175 -18.97589 -40.26837 -20.87660 -22.41765 2.39376 7.14876 1.39583
[51] -47.26142 -35.71709 -0.70272 -44.02984 -11.78579 -18.72828 -25.52016 -35.14329 -22.67467 -25.17291
[61] -0.50657 -2.65407 -1.73057 -9.26134 1.04842 -17.43226 -20.29612 7.15463 -11.48298 3.89165
[71] -10.03687 -17.17986 -12.71297 -32.31703 -2.80811 -28.09789 -1.88708 -26.21944 -20.51237 -29.76852
[81] -8.11528 -13.50990 -20.38195 -6.95576 -17.96194 -24.52757 -19.47918 -10.53702 -23.40539 -21.22797
[91] -24.50250 -14.92554 3.00214 4.64755 -7.20061 -25.54565 -20.42301 -11.08873 -21.64976 -25.50318
[101] 11.13058 -16.11606 -13.07275 -20.15522 -7.69137 -21.86173 -27.17182 -16.76477 -24.33522 -21.25146
[111] -42.13555 -7.90353 -20.64216 -27.41937 -9.33038 -25.19000 -3.47618 -47.23524 -39.03591 -5.99887
[121] -35.29561 -61.85269 -16.31523 -33.06003 -40.04740 -8.68174 -0.62181 -61.65123 -41.66083 -53.48208
[131] -27.03090 -30.69510 -45.09963 27.12286 -40.67376 -23.67652 -52.32280 -42.34535 -7.13526 -9.01643
[141] -37.87415 -48.36485 -19.72608 -20.40798 -38.40346 -25.16232 -12.50700 -3.36188 12.43114 -33.25953
[151] -27.73813 -50.37212 -23.89048 -19.81560 -24.65750 -13.78283 -27.35446 -1.99263 -20.17228 -9.22648
[161] -16.73277 -24.39275 -10.78803 -28.02217 -22.58134 -4.68893 -33.03993 -20.31110 -13.83125 -31.93079
[171] -7.50587 -37.00663 -0.76904 -24.99494 -31.57197 8.80152 14.55934 -34.23282 -22.64780 -20.60818
[181] -26.83139 28.99298 -5.64255 -36.52907 19.21302 4.38446 -35.26799 -43.19494 1.33225 -58.77524
[191] -3.11285 -23.55510 -26.84706 -20.70460 -59.21539 -46.30264 -45.32562 -28.72835 -31.41872 -6.45785
[201] -11.37352 -29.27702 -23.72231 -10.96955 -28.64547 -21.86596 -32.23255 -20.41633 33.62041 -61.07884
[211] -18.81839 -31.42549 -15.49383 -0.34064 5.35521 -31.85330 -43.51691 -33.38458 -7.98612 -22.36017
[221] -31.09804 -60.83112 -46.22059 -36.25814 -25.01913 -29.37346 33.17562 -39.25451 7.63121 -10.00130
[231] -36.43561 0.60660 0.73191 -36.24088 -3.29584 -4.65505 -16.58725 -36.69077 -50.63362 12.77286
[241] 21.94846 -38.69097 0.04184 -16.79344 -35.01369 -34.77301 -20.50019 -13.10262 -13.36356 -37.63734
[251] -60.92641 17.73297 -31.45399 -34.28495 -27.09484 -24.20261 -25.23063 -14.04545 -4.49223 24.26257
[261] -6.32531 -37.71085 -21.45111 -43.90957 -34.20970 -6.62407 1.18734 -25.73293 -13.93953 -20.74891
[271] -1.85090 -19.61512 -27.35403 -5.38823 -16.44750 -44.28207 -23.81744 -30.30841 23.15980 -13.06967
[281] -6.62368 -21.04634 -13.95191 -39.42531 -23.63905 -10.96155 -16.06486 -35.90460 -3.25266 -21.68159
[291] -27.33205 -10.31259 -21.04046 -23.19851 -20.38271 -20.52724 10.32047 0.53199 -4.05690 -37.01743
[301] -18.11837 -33.14022 -29.86419 -17.91400 -31.62731 -46.57273 -12.21205 -46.09870 -30.56472 -24.74691
[311] -23.87835 16.01377 -17.45532 -11.95324 -19.14936 -12.20489 2.75872 -21.70901 -11.22982 -27.65555
[321] -13.39586 -15.49027 -19.80345 -8.97868 -25.74272 -13.11433 -23.22414 -31.65440 -11.54780 -35.00340
[331] -10.90765 -34.37972 -28.51202 -24.35955 -26.60262 -12.16501 -20.99941 -20.18446 -13.17888 -47.16572
[341] -43.49894 -36.26677 -54.38828 -27.82011 -15.54964 -12.19502 -44.93739 -16.51760 -19.34187 -12.04002
[351] -9.46549 -42.83512 -11.86695 -44.37837 -25.74988 -19.53021 6.37822 4.52173 -9.53726 -12.36464
[361] -45.20222 8.44316 -33.39023 -24.79680 -49.26174 -33.55441 -27.02360 5.40203 -14.75932 -33.14036
[371] -27.74808 -26.24840 -6.70973 -18.81239 -16.56273 -15.84590 -1.39521 -9.84160 -35.78880 -38.00686
[381] -26.96687 -21.84446 -14.01041 -26.89055 -47.55926 -26.31825 -46.66904 -22.78997 -34.36908 -43.08690
[391] 15.83545 -26.51927 -24.63361 -6.38446 -14.56413 4.92922 -38.91560 -11.17122 -1.84148 -20.84209
[401] -13.56062 -9.62860 -6.10217 -44.68162 3.99802 0.41179 -26.94150 -33.91520 12.17826 -6.13837
[411] -7.48761 -22.49120 -50.44970 -19.67265 -30.13812 -27.15335 -35.78490 -13.77279 -14.52755 -23.11338
[421] 7.99573 -43.30904 -15.47218 -27.78527 -44.33184 -31.09581 -29.19707 -20.33022 -7.23501 -18.73779
[431] 5.25491 -23.83976 -44.09833 -35.27371 -17.48516 2.24223 -47.96688 -15.32165 -5.00824 -16.21291
[441] -38.48260 2.39252 -4.15525 -8.07520 -20.27009 -39.20920 -14.87781 5.59844 10.00646 -5.50369
[451] -6.07113 -12.47786 -53.38887 -35.91870 -2.08767 -19.99054 -39.06654 -30.70088 -28.44251 -38.75415
[461] -14.97053 -13.54555 -19.96294 -6.68634 -16.24230 -17.96254 -23.60348 -13.05144 -28.97323 -41.67291
[471] -43.94751 -11.37182 -29.56281 1.02630 -23.48838 -17.04101 -24.55288 -5.35796 -11.98875 5.98980
[481] -14.60721 9.28268 -33.91872 -27.87320 -3.86112 -25.25144 -25.32270 0.20617 -29.66612 -44.40420
[491] -57.97369 -5.36438 -4.95824 -13.53727 -12.52840 -25.20109 -15.13518 -51.19278 -22.60861 -18.20870
[501] 11.19257 -4.63097 -21.09910 -18.27261 -7.36220 -24.64910 0.64761 -28.48468 -17.19355 -24.56598
[511] -3.43624 -19.89127 -11.46586 -0.40689 -29.78073 -4.24026 13.13451 -18.01409 -3.27022 -15.09920
[521] -40.75504 -11.97323 -27.71885 -49.78997 -57.66022 -25.40543 -19.49406 -36.32147 -46.75500 -38.76536
[531] -12.99420 -31.59686 14.98321 -11.11627 -35.79368 -5.56559 -11.10526 -42.11933 -22.40724 -1.06548
[541] -39.28853 -16.53822 -36.02337 -57.86113 -12.41474 3.88645 -1.13554 -26.58744 -31.84920 -16.52263
[551] -14.37394 -62.27317 -15.81127 3.28754 -28.98940 -15.61139 -35.76431 -30.44422 -37.08764 -25.42892
[561] -15.91742 -12.16047 -35.25078 -31.54451 -33.20939 -17.39785 -32.93305 -24.78752 -36.27819 -13.54057
[571] -46.67527 -36.68015 -19.46591 -15.16711 -23.47302 6.94022 -18.71276 -28.13631 -19.46064 -1.83809
[581] -15.22395 -34.30397 -2.18986 -32.78756 3.10229 -37.45208 -7.63015 4.77363 -42.91472 6.17501
[591] 0.07548 -16.47234 -10.28799 -19.44233 -19.45921 -47.02399 -4.55208 -8.49014 0.90934 -28.86146
[601] -24.72674 -22.32137 2.33850 -37.19286 -3.02854 -12.89593 -19.76881 20.52432 -24.12840 -4.74084
[611] -16.98166 -38.95492 -26.52929 -29.26171 -28.44603 -39.40880 -11.40694 -39.05061 -18.87705 -38.06207
[621] -30.55623 1.15500 -11.92701 -31.25059 -24.40530 -28.16242 -3.42314 -28.30652 -17.33691 -50.89484
[631] -11.72195 -32.21901 -39.40549 19.13042 -22.34903 -26.11761 -29.47105 -9.30030 -18.53750 3.02518
[641] -22.34358 -10.81746 -33.57657 -22.34866 2.84860 -26.36589 -34.25748 -10.32650 -7.07460 -36.79128
[651] 3.93782 -38.94444 -42.10226 -23.39433 -16.02512 -15.09043 -35.89840 -13.89460 -31.03753 -7.51150
[661] 4.81620 -23.78919 -3.39755 -17.24389 1.59250 -6.94309 -20.97250 5.69577 -9.81385 -21.10706
[671] -27.58972 -33.98208 -20.78815 -21.29318 -9.40457 -66.54288 -24.51641 -33.04812 -23.64883 -21.75009
[681] 0.73540 -24.75086 -20.90443 8.41424 -32.71025 -15.91832 10.87985 -21.55321 -45.15703 -29.11600
[691] -9.21337 -24.25629 -7.90129 -13.12002 -12.99296 -49.78293 -1.23356 -15.96571 -23.50895 -22.69716
[701] -6.96772 -22.94041 -38.53718 -58.55726 -4.98132 -51.05118 6.16163 -11.72005 -20.19649 -9.84647
[711] -1.36153 -9.97492 -37.82198 -5.65952 -20.37201 -21.93195 -55.70008 -33.73665 -16.02173 -34.42705
[721] -31.47826 -32.04373 -13.37714 -23.23739 -21.31706 -8.57823 3.07223 -17.62472 -44.97780 -28.59966
[731] -12.74834 -19.16190 -40.06595 -51.02756 -34.49882 -58.09728 -8.59807 -54.93950 -31.26724 -8.80499
[741] -15.20951 -31.89230 -10.19734 -13.06538 -46.58920 -52.78970 -30.90480 4.65841 -23.64628 -4.34041
[751] -21.81306 -21.70252 10.58018 -34.62581 -26.66712 -17.48641 1.19595 4.31790 -26.98086 -6.77289
[761] -15.77848 -15.53447 -36.00233 -16.41273 -18.38031 -21.45494 -16.38114 -25.10203 -5.69489 -3.23795
[771] -29.35990 -32.27348 -18.99448 -31.47673 -50.61456 -18.81009 -25.86430 -29.64113 -5.84481 -20.46607
[781] 10.37991 -25.57893 -43.78698 -13.32596 -8.23096 -37.00055 -25.35604 -5.86072 -27.29687 -0.48841
[791] -7.15930 -8.15663 -17.09932 -6.11027 -4.12015 -13.08836 -32.91704 -39.53651 -42.24379 -35.65031
[801] -44.34649 -7.81423 -21.24295 -17.10757 2.28514 -32.98070 -24.85497 -10.65769 -7.62813 -17.57094
[811] 15.70680 -18.17921 -44.11096 -19.42440 -52.73989 -7.53145 -6.01140 -9.33237 -44.30548 -18.90400
[821] -19.48719 -22.17985 -30.20951 -22.97606 -25.19150 -33.60446 -33.17107 -68.76233 -14.44526 -16.14729
[831] -5.93924 -38.58683 -21.24243 -44.06328 -21.16556 -39.71380 -1.36448 -3.13649 -38.63809 -19.11934
[841] -31.65152 -18.05493 -41.77041 -6.25583 -24.19724 -43.72714 -18.44537 -58.72406 -40.21309 -40.22242
[851] -2.14782 -45.42743 -15.19872 -19.89354 -6.10678 -0.18041 -44.78073 -19.24607 -0.06810 -17.78125
[861] -58.30230 -10.40872 -17.00294 -40.76609 -2.68085 15.53831 -43.90669 -7.30956 -35.35776 -39.33276
[871] -22.35506 -23.53042 -56.25923 4.17221 -26.87756 -11.73811 -5.55146 -27.46072 -24.55612 -35.07348
[881] -13.33364 -23.49070 -4.12418 -13.92962 -19.85207 -6.99353 -28.98018 -39.48200 -40.31791 -53.81739
[891] -0.91816 -11.14657 -32.91532 -10.23921 -2.23213 -25.20885 -7.41430 2.41450 -17.26597 -33.62661
[901] -29.31389 -23.75694 -20.26744 -21.56927 -16.24884 -1.93749 -14.42621 -21.46492 -41.17690 -15.36296
[911] -16.51955 -22.94950 -21.62614 -36.96359 -9.26122 -6.01828 -15.91384 -16.59503 -19.45556 -49.89155
[921] -50.66363 -23.16990 -13.23625 -22.25945 -17.87204 -21.32124 -11.17846 -31.42424 -25.25672 -2.98681
[931] -17.26051 -21.10476 -10.26911 -15.68548 -9.09234 -17.50595 -32.33365 -26.40359 -7.86356 -14.48394
[941] -29.91467 -10.94372 2.08461 -18.15117 -23.45498 -24.35419 -14.60036 -27.93869 -15.50684 10.61345
[951] -49.63467 -6.67226 -8.09188 -13.84087 -30.95266 -9.65144 -20.93771 -4.46800 -16.25365 -2.21627
[961] -32.12069 -35.34032 -22.25100 -15.62396 -19.53970 -36.55948 -18.25886 -14.21196 -19.78934 -28.57425
[971] 3.67296 -18.26578 -28.77689 -18.17131 -32.69687 -27.45477 9.62470 -0.27869 -45.87856 -45.83430
[981] -14.59774 -30.05189 -16.20796 -59.15906 -29.55102 -23.09375 -8.28151 -12.00420 -42.39101 -34.32105
[991] -15.14867 -13.08025 15.79383 -28.95502 -20.61047 -30.68996 -12.08274 -18.35118 -9.04691 -22.70079
[ reached getOption("max.print") -- omitted 7000 entries ]
density_synt <- density(pst_synt)
plot(density_synt, main = "Density Plot of pst_synt", xlab = "pst_synt values", ylab = "Density", col = "red")
pst_gramxlex <- y_posterior$Gram_x_Lex
pst_gramxlex
[1] -11.08280 -6.08654 -17.04219 7.76664 -0.32583 4.98926 -6.99529 -10.58977 -3.74560 -0.06514
[11] -14.54703 -6.99544 -7.76189 -20.65629 -10.50493 -25.90487 -16.71278 -12.14217 -23.89411 -13.22386
[21] -12.33252 -8.29381 -25.32766 -13.02649 -15.50731 -3.01404 -2.11874 -9.37160 -6.76210 -3.80437
[31] -13.22596 -3.07535 6.93447 -8.82166 -14.84416 -26.26701 -6.71855 -14.27618 -12.41271 -3.00698
[41] -15.29811 -30.89310 -6.75717 -17.76977 -4.55855 -22.03309 -9.04184 -7.12720 -7.09147 5.42318
[51] 2.00446 -16.94352 0.76287 -18.71466 -25.31994 -7.64947 -8.89003 -11.03067 -0.23393 -10.10203
[61] -13.79092 -16.92184 -2.07346 -7.45063 -0.12170 -10.34707 2.00036 -8.66828 -9.85124 -6.47480
[71] -13.62523 -21.69970 -3.53309 -4.87064 11.31213 -11.17397 -2.14618 -22.81392 -22.28687 -8.22641
[81] 2.75818 -6.15772 0.73978 -12.87762 -4.52259 -18.13325 -23.21772 -2.60416 -3.05531 26.35757
[91] -4.72542 -5.36157 -9.26975 4.34086 1.29011 -5.75697 -8.61314 -9.19346 -13.30402 -2.99624
[101] 3.25198 -15.18022 -10.33424 -1.67478 -15.24370 -5.16032 -12.20625 -19.69192 -5.68986 -13.97159
[111] -11.50351 -8.54170 -2.48600 -15.68126 -15.11498 -11.83791 -13.79225 -0.48374 -7.03722 -14.26591
[121] 4.94865 -11.63486 1.83680 -4.15059 -8.16602 -10.52790 11.48945 -3.44841 -12.39657 -3.66313
[131] -23.99062 -6.53397 0.07796 -26.06522 -4.59210 -13.59520 -16.17098 -11.41517 -12.86337 -9.67061
[141] -21.87614 -0.15224 7.54642 -7.45027 -10.49930 -25.43459 -9.41680 0.76855 -7.62924 -7.02371
[151] -3.29645 2.78191 -25.80960 -34.28899 -19.14774 -13.17859 -23.42572 -5.28418 -18.46439 6.54554
[161] -11.96944 -10.17661 -3.85109 -8.14928 -4.16733 -14.89737 -7.30955 -30.15883 0.03682 -7.56035
[171] -5.52645 -1.38173 5.18603 -16.90540 0.68987 -14.38552 -9.73432 -12.54884 -14.68861 -7.31741
[181] -3.21254 -11.39522 -12.45271 4.82797 -21.54590 -25.72283 -23.77677 -19.23961 -25.50597 -24.93399
[191] -17.08541 -17.43908 0.60626 -14.86346 -3.11639 -12.97578 -6.71095 0.20881 2.13139 -10.07740
[201] -4.76982 -23.06568 -8.70241 -10.58015 -4.76932 -9.82113 -15.48771 -12.62444 -14.01491 -7.11118
[211] -26.78723 3.38389 -6.38233 -12.00129 -23.13706 -6.66229 -19.15944 -8.00509 -12.77143 -23.53392
[221] 0.25577 0.59464 -11.42155 -9.45258 -10.72829 -3.32999 -0.18340 -6.67195 -8.87881 -16.81241
[231] -28.60706 -17.87764 3.00827 -13.38061 -9.75908 -1.43186 -9.10538 4.22714 -15.64861 -2.31562
[241] -23.47033 -15.17662 -2.18224 -10.89571 -0.50625 -13.31124 -17.29188 10.54767 -39.29760 -10.02728
[251] -14.33420 -16.01155 11.89078 -24.36071 -11.71741 -13.88292 -29.94694 -3.07941 -15.57355 -6.28970
[261] -18.31991 -9.84941 -13.35457 -5.02919 -9.64328 5.57382 -18.82820 -21.22498 -11.94711 -25.92636
[271] 11.43854 -18.27723 -6.02673 -7.58474 -21.55182 -6.35768 -11.39573 -18.83192 -27.77062 -9.26056
[281] 9.07170 -4.44639 -24.38567 -11.12728 -9.92234 -5.04926 -6.94229 -3.32099 -12.33312 -16.10484
[291] -7.12478 -18.84269 -10.04503 -5.74118 -17.49090 -16.35831 -25.30949 -1.53215 3.48769 -4.35437
[301] -18.71864 -10.79530 -14.77440 -8.72793 -7.39130 13.60423 -5.74622 0.35518 -16.26410 5.24040
[311] -12.89293 -20.28800 -8.90661 -14.50790 -9.69912 -8.07590 -8.98401 -16.92210 -9.68588 -19.32864
[321] 0.87023 -15.25671 -16.37896 -6.72738 -20.63030 -21.07194 -7.14814 -14.55891 -11.02929 -17.41432
[331] 5.88068 -20.35850 -3.35870 -5.81921 -4.07108 -6.82461 -15.98549 -2.72462 -4.16649 -5.52780
[341] -18.91642 4.44987 -0.39758 -7.24134 -2.86723 -0.93993 -16.61832 -10.04806 -17.11601 -16.02527
[351] -9.89715 -19.83407 -9.85706 -4.74392 -10.99618 -4.04720 -3.18148 -21.03600 -1.06165 2.91694
[361] 3.88003 -10.95138 -2.78221 -23.25480 -10.62253 -13.12851 -7.05198 -20.19985 -10.59192 5.77002
[371] -11.63345 -6.15174 -8.37273 -9.25222 -9.88393 3.74481 4.39399 -8.53077 1.17318 -1.44057
[381] -26.80731 1.38498 -11.96895 -28.18535 -10.16310 2.64721 1.48176 -4.46050 -6.57647 -2.17727
[391] -10.54479 -12.90808 -15.48221 -9.02714 -0.50654 -6.71943 11.42145 -5.46784 -4.68606 -17.99849
[401] -17.73361 -11.85891 2.96250 -21.87981 7.00383 -13.57211 -20.16226 -21.66169 -4.62116 -18.21317
[411] -12.68783 -23.76064 -15.91971 -9.95856 8.76671 -16.07861 -19.61040 -5.66202 5.24045 -6.06803
[421] -13.48623 -12.43173 -9.88906 -3.61533 -15.43913 -17.29537 -17.69554 -0.35360 -12.26617 -10.04655
[431] -3.20959 -5.20022 -23.60164 -16.50657 -9.39616 4.16322 -12.18118 -26.71733 -14.25799 -11.25066
[441] 2.71351 -14.55656 0.24994 -1.96454 2.72647 -6.50379 -13.45644 -14.49271 9.14819 -13.36813
[451] -2.75456 10.54917 -6.67214 -17.76054 -14.27582 -6.26888 -12.17748 -11.48780 -9.13193 -0.89332
[461] -15.31045 -6.97502 -24.16436 -24.23144 -14.51255 -3.18041 -21.14783 -3.76852 8.75757 -2.26915
[471] -19.18211 -27.36611 -16.99069 -12.50369 16.68466 -4.55582 -5.79849 -15.20503 3.94415 2.15708
[481] -1.10498 -9.00407 -6.55428 -19.86286 6.07111 -18.77434 -20.86775 -13.14598 -2.76136 -10.77018
[491] -3.99846 -28.54187 -0.18223 0.17132 -5.10208 -9.21793 4.00227 -9.86344 -7.50296 -9.38078
[501] 7.72605 1.69071 -3.70008 -9.99601 -1.29277 -7.36798 1.12692 -20.63738 -4.13725 -22.50630
[511] -29.03635 -27.25299 -13.32766 -7.95343 -3.04714 -10.23562 -21.12549 -12.33987 -20.18263 8.16119
[521] -17.12236 -14.57439 -12.75331 -32.89061 -23.04698 -2.03969 4.93915 -15.85628 -9.72704 -17.26462
[531] -13.65250 10.49375 -1.33830 -10.87912 -1.59589 -4.47098 -14.30623 -16.50827 -7.36550 -13.57149
[541] -3.36353 -10.57110 2.44292 -8.80537 -19.07385 -19.45939 -20.67716 0.80730 -2.70263 -10.46810
[551] -5.83186 -9.20088 -14.26603 -20.80125 -6.07376 -9.26067 -25.36502 -5.26524 10.30146 -5.62392
[561] 5.65336 -1.10229 -8.66395 -21.90737 -12.36346 -19.83287 -7.19654 -14.83119 7.62498 -28.46925
[571] -0.18910 -13.53638 -16.54204 -8.18663 -3.97941 -6.73923 -8.20265 -8.09806 -10.59554 -0.08454
[581] -10.67756 -7.39254 -4.91080 -14.16270 -4.31856 -3.95288 -21.28618 -0.42638 11.23264 0.65814
[591] -6.00070 -22.18816 1.85836 -14.40884 -0.08279 -25.98121 -2.85354 -7.91139 -16.64449 -16.22455
[601] -0.37557 -15.31186 -15.81296 -4.69941 -9.68820 9.17577 -10.78678 -21.15587 -10.84749 -10.31658
[611] -18.94301 -7.13505 -3.71448 -13.88901 -19.30800 -21.34242 2.53289 1.25496 -8.81499 -11.81149
[621] -20.00478 -16.01828 -12.22957 1.94896 -17.84611 -10.43206 -7.65006 -18.23001 -7.59199 3.45741
[631] -12.32927 -11.50362 -9.55849 1.59153 -3.10258 -10.29148 -6.14636 -24.37288 -5.15046 -7.60405
[641] -10.45811 -6.53578 -15.13164 -3.03771 -19.15458 -0.04432 -11.99908 -0.32025 -27.78575 -9.06957
[651] -18.81634 -16.83028 -3.67537 2.48431 -22.87968 -5.85969 -7.63711 -11.61068 -12.37136 -2.56205
[661] -2.42483 -20.90540 -11.02077 -22.60569 -13.71824 -17.01228 -13.66280 -2.58627 -2.82543 -4.51321
[671] -22.92800 -18.20036 -10.96012 -18.02768 -17.65806 -11.50105 -16.90380 -5.78791 -8.77659 -3.09499
[681] -16.77349 -0.24174 -5.65577 -10.62662 6.11170 -24.06344 -28.58249 -10.33702 -23.65367 1.96860
[691] -17.09330 -9.72008 -22.09085 -10.88672 -6.13099 -11.65006 4.83747 -20.06165 -10.80636 -9.68036
[701] -9.52355 -2.57491 -5.87232 -22.61424 -19.87235 2.81585 -4.11223 -10.12017 -5.49102 -17.70410
[711] -3.74859 -16.25900 -8.52936 -32.68030 -17.32294 2.95291 -12.49487 -3.71006 -3.60520 -18.26989
[721] -3.78815 -6.99734 -20.71325 -10.54190 12.57897 -20.77675 3.41824 -23.91922 -10.52875 -5.52893
[731] -7.60805 -15.65198 -19.83230 -21.04243 -21.32242 -8.92506 -5.96735 -11.57556 -11.84872 -13.89458
[741] -13.78559 -20.70129 -9.67035 -6.17604 -11.67093 -21.28363 -6.10481 -9.76233 -3.87414 -12.38473
[751] -5.09527 2.69713 -7.18110 -25.37977 -30.79423 -0.43096 -0.50777 -12.90881 -24.11727 -13.27833
[761] 4.93520 -16.59259 -14.90532 -17.76146 -16.94168 -24.21714 -2.41745 -21.49588 -18.19651 -19.18167
[771] 26.62805 -3.90927 -8.83975 -10.85321 -26.27467 -23.11637 -16.30323 -15.00900 0.48189 7.94392
[781] -16.68108 -4.45070 1.71859 -26.44843 -5.58010 -6.17975 -11.41604 -15.13558 -8.16104 -8.63550
[791] -9.83710 -14.35381 -5.19395 -16.75213 -22.81257 -12.40660 -6.85898 -1.56773 -6.84348 -13.57445
[801] -8.64246 -23.95451 -1.61754 -9.82443 -2.63632 -1.39525 1.67373 -7.09858 -5.13594 -4.62313
[811] -5.32645 -13.12205 -29.59073 -15.42300 -6.17675 -8.52224 -4.44272 -24.87501 -16.46662 -6.73669
[821] -21.49467 -21.68990 1.56183 -13.03799 -31.00383 14.83748 -11.51240 -11.21921 -9.04493 -4.60955
[831] -7.25348 -17.48828 -13.47441 -11.69169 -12.49356 10.12094 -18.24067 0.83593 -8.77465 -20.56048
[841] -15.75145 -6.60663 -13.69602 5.53261 -10.97067 -25.88300 -4.68172 4.11551 -2.09063 -6.97928
[851] 11.54985 -24.14892 -12.99265 -15.05715 -26.35646 -9.49165 -17.55231 -7.54939 -20.51274 -7.29464
[861] 2.24866 2.40184 -6.88408 0.55948 -7.41537 -27.59101 -24.01985 -11.17612 -24.59056 -9.98298
[871] -18.94422 -19.27632 -5.69466 -6.43853 -11.47399 -15.06406 1.38486 -4.21842 -14.90408 -9.07737
[881] 0.19532 -11.35078 -19.56560 -9.25288 -14.64436 -25.56167 -26.14363 -4.03614 -10.80604 -1.22518
[891] -0.62535 -4.00090 -16.10513 -14.76173 -25.49890 -12.74796 -22.71360 4.39479 -4.62313 -9.62846
[901] -12.59034 0.55474 -15.82509 -2.62724 -19.19964 -5.90592 -14.00621 12.30464 -7.01888 -6.68624
[911] -3.50736 1.53301 4.75640 6.92010 -4.95960 -4.10576 -4.22065 -17.69833 -12.86365 -6.67419
[921] -9.60538 -9.87605 -15.24612 -26.54749 -14.44399 -5.50619 -12.49120 -15.58132 -3.07279 -5.67746
[931] -8.97394 -9.69684 -0.05886 -24.63628 -9.15756 -25.56488 -4.30014 -17.30616 -2.02380 -17.40122
[941] -11.90749 -4.83646 -0.11739 -2.17642 -3.16044 -16.99101 -14.28239 -1.04174 -3.40694 -14.29376
[951] -0.13732 -2.34037 6.57413 9.01424 -10.70834 -20.99154 -18.96958 -4.44755 -0.81690 -20.88665
[961] -22.00074 -11.59618 -20.25990 0.07973 -13.08686 -8.77299 -6.18519 -21.41187 -13.55368 -1.71078
[971] -5.08888 -12.10784 -19.59959 13.14861 -13.30264 -3.08658 -8.40427 -4.70066 -27.94099 -15.56069
[981] -7.88713 -7.30523 -24.39129 -21.29654 8.70596 2.21960 -4.43285 -10.80261 -3.24430 9.25269
[991] 0.04128 -6.02727 -19.69384 -8.11925 -13.66681 -13.55660 -16.52119 7.44623 -29.79066 34.80182
[ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxlex <- density(pst_gramxlex)
plot(density_gramxlex, main = "Density Plot of pst_gramxlex", xlab = "pst_gramxlex values", ylab = "Density", col = "red")
pst_gramxsynt <- y_posterior$Gram_x_Synt
pst_gramxsynt
[1] -5.053453 6.187288 2.788221 1.284608 -4.870128 -6.332632 -3.613579 3.916330 7.211630
[10] 7.799748 -2.643932 -3.106716 3.015732 -1.024004 3.825743 4.259845 -10.983481 -9.339822
[19] 12.959679 6.541848 8.628574 -19.175502 27.641577 -1.544770 6.282753 -0.735286 -0.473835
[28] -6.199372 13.784847 -2.840982 8.067476 -7.237985 -8.564921 12.606500 5.555969 11.344557
[37] 5.943935 10.006514 3.015910 3.455928 11.921744 12.284612 0.308969 19.797172 1.918032
[46] 0.145443 3.852571 6.191142 1.228013 1.174749 0.247189 0.102489 -6.936929 0.878073
[55] 11.062615 16.392734 11.407588 28.730324 -4.287731 12.664581 4.504808 13.951294 -2.830624
[64] -2.165854 -21.729867 -1.512891 -16.866201 13.026079 2.497641 5.595442 -0.875782 1.781424
[73] 9.637527 0.631284 -12.888804 6.355983 9.994239 13.456519 -8.583225 4.242755 -4.019064
[82] 3.189612 -8.103400 26.863973 -9.095801 19.069311 -8.410639 4.441780 15.847373 -3.726269
[91] 6.071552 6.551298 -0.606627 -4.319927 -20.682455 5.685297 4.973333 1.660707 -6.152877
[100] -3.719542 16.713791 20.845728 7.773434 8.382857 14.904498 -0.605131 17.400956 9.785776
[109] 3.597514 10.888214 5.469694 2.483437 -6.577348 0.864946 9.195712 14.132052 7.406341
[118] -2.393484 5.079770 3.961647 4.042530 15.335220 3.774312 10.009882 12.582455 5.485260
[127] 2.045588 -7.816798 3.562309 9.432307 18.465854 -2.486680 -0.228495 13.930571 12.628623
[136] 19.392177 7.757399 21.378542 -11.067366 -7.494346 2.371555 -6.540370 -6.033924 7.914687
[145] 5.101072 19.699693 9.704806 -4.235995 4.355751 2.787770 9.549229 -4.539028 17.531405
[154] 20.284718 12.329194 -2.706850 8.241642 7.627439 19.492538 -23.548634 -1.004127 2.284058
[163] 11.915772 2.532465 -7.843447 10.366107 3.604952 22.241983 2.027551 12.019639 -2.149810
[172] 0.955109 3.482669 0.663005 8.796421 -9.734590 8.506266 5.484685 6.227016 2.697402
[181] 19.287776 19.861910 -11.175707 6.049371 22.623906 9.995620 -10.211583 -0.271583 4.162394
[190] 13.432062 4.207886 -2.112518 0.150159 -4.654543 -3.443783 5.245484 -2.259995 2.774097
[199] 7.813589 14.812771 -1.988457 1.994787 -3.995156 3.473761 3.508531 3.455824 11.349462
[208] 9.894377 5.403925 20.375832 19.608787 5.164285 -0.290915 -14.175791 2.050422 19.904954
[217] 12.788805 8.102365 0.287716 14.407251 -10.444507 7.054463 5.198207 9.477967 14.179353
[226] 4.684244 -4.266322 6.922544 8.215692 7.301607 20.094777 15.694471 15.993304 17.012609
[235] 18.260354 7.328896 4.283168 -2.291487 2.777631 0.775336 9.548955 6.559970 -7.379430
[244] 6.325941 -6.431213 21.182812 6.218338 5.947482 18.640357 -1.351808 8.462403 6.992952
[253] 3.664319 19.233249 2.899178 1.535741 25.139058 2.841079 1.213712 5.547729 8.783602
[262] 6.908939 -0.091830 3.224392 -5.788026 2.826116 1.519525 -7.765002 8.493097 19.977919
[271] -0.486242 9.558352 2.002735 7.675204 8.752569 -5.093942 2.877616 15.409736 10.103208
[280] 3.336653 -5.400225 -2.423008 -1.526734 -13.906285 16.009958 -1.162662 -4.959278 16.996247
[289] -2.019232 1.964645 2.759623 0.043366 1.327680 -8.706122 13.274514 -4.936797 2.659285
[298] -1.512589 -0.294874 6.404691 18.796568 9.428658 8.219358 8.899932 -3.546023 -9.899052
[307] 1.965884 9.535152 -0.351653 -8.208483 4.568598 3.492569 -0.430720 -7.955663 9.799567
[316] -21.958958 0.067696 27.516985 -2.688834 7.934065 12.272655 1.170769 -3.133768 -1.318990
[325] 9.569706 14.969196 -1.956410 16.811704 0.160913 6.049986 -4.029243 10.554711 3.105128
[334] 3.629912 9.884248 8.627549 10.816349 -5.030813 -5.997755 10.147918 15.785601 -1.311451
[343] 7.133458 2.989098 8.884431 -2.347602 14.892991 2.421884 7.163049 -4.874137 1.305174
[352] 10.627507 -4.155432 6.176532 -1.322800 14.024713 0.568358 18.763436 -12.333700 -1.006919
[361] 1.977944 2.437857 2.098135 12.671817 3.213519 -8.483142 7.966549 17.372031 -10.508921
[370] -4.235374 2.550647 0.448104 3.902981 3.124080 -0.993473 5.760251 -0.548848 -7.340884
[379] 5.584966 12.245191 15.573070 -6.598894 15.763066 23.671780 3.013174 -2.736038 -2.421748
[388] -5.660911 -4.268690 3.044785 2.422531 -1.704197 2.988869 4.077711 6.341435 -6.349027
[397] -2.251408 -1.811620 4.125453 18.976401 3.214969 3.074487 2.677881 -4.881523 1.833165
[406] 18.040605 9.751776 13.333837 18.255246 -3.189583 -1.991010 13.039830 6.144306 16.125345
[415] -4.124313 12.892118 7.829574 0.954264 -3.821196 10.606780 9.064349 2.475111 -0.347580
[424] -8.557615 7.055253 5.629817 27.635489 -10.543477 -0.741416 7.692615 -0.222345 -3.245987
[433] 3.267856 12.454894 9.440006 12.657657 11.303053 4.631319 0.902379 8.075658 5.818455
[442] 6.288345 -13.256888 1.737642 9.517268 14.521608 5.182836 16.173184 -2.360418 5.721045
[451] 4.986295 -7.084212 0.958797 6.155434 -5.333158 -2.041216 3.123794 14.143095 5.531897
[460] -2.620152 -5.702093 -4.931056 17.739409 21.886783 -5.449530 0.310195 21.781052 15.007412
[469] -4.696156 -1.432897 20.038847 15.436156 11.283308 -4.860775 -7.353670 4.733178 3.043015
[478] 2.799513 9.834391 8.391123 1.277970 -6.122896 -4.962989 25.508084 -7.717435 14.207182
[487] 17.838391 19.428274 -4.812144 -7.945381 2.251566 2.487607 -0.002364 12.629385 10.885681
[496] -5.232177 7.663254 19.422900 -1.059164 -8.704393 0.159366 23.095980 14.822701 1.832953
[505] -5.953754 12.345220 6.261403 17.237732 -0.734317 14.811900 28.598327 12.981526 -9.962202
[514] 8.513484 -0.889745 16.373108 9.158000 -3.408188 7.680220 -9.363283 16.090447 10.043217
[523] 2.746883 23.472436 12.805661 -12.517864 -6.557431 23.705527 9.005702 1.948991 24.532423
[532] -14.184604 5.817268 7.035089 -1.496171 5.874163 2.668661 3.817875 5.918923 4.632779
[541] 3.571793 5.316943 -5.335769 16.851643 19.601852 11.295398 9.688587 -14.697673 14.310540
[550] 3.202705 -12.199866 -8.082167 4.922504 5.447594 15.396581 9.833449 25.784216 12.717465
[559] -2.471140 1.368212 -12.856060 -1.141232 5.831997 11.831274 6.964628 13.222256 1.826584
[568] -1.803269 1.739197 -7.391400 -19.676821 1.418664 6.170360 11.433315 18.679056 10.429916
[577] 1.089153 7.766909 7.150399 13.463836 7.383111 -0.428451 8.986215 -6.312528 17.073858
[586] 25.841547 16.451061 1.439090 -2.660281 -0.967140 -6.694129 21.065100 -7.786669 -2.479409
[595] 2.377931 20.000263 10.938845 3.631645 7.303160 -8.328875 -0.654927 14.645951 6.273834
[604] 0.522520 -5.995467 -8.103050 4.813131 1.636077 1.362243 10.534795 18.323837 12.507997
[613] -6.871509 13.402546 13.536620 5.503037 2.175402 -7.231038 1.415712 13.186322 10.548349
[622] 16.419156 15.032837 -7.613518 6.620033 2.571275 6.914560 3.526747 2.140931 -1.237016
[631] 5.929109 -2.538458 1.444144 -7.651389 -2.286604 8.428135 -4.935518 9.211989 -0.014994
[640] -4.247242 8.357060 17.159924 4.456236 -2.946552 15.916110 -5.383668 2.688395 8.151128
[649] -2.700233 12.350878 28.148408 21.185762 13.165640 1.125888 10.159383 3.054255 7.305171
[658] 8.445932 6.896199 0.203805 12.987687 5.620717 15.626413 11.663436 6.877827 8.077279
[667] 4.668451 0.015477 9.984810 -2.084865 11.760378 16.255061 11.815446 0.786651 9.726244
[676] -2.323269 13.767656 -6.573214 13.470867 1.563920 6.301512 5.006630 7.239981 10.116298
[685] 7.797837 5.843406 19.191706 11.087234 25.614871 -15.980162 18.960213 12.199922 20.179291
[694] 9.843779 -9.570758 1.042982 1.193888 12.653550 8.809818 13.095755 16.630396 -3.519898
[703] 9.752238 15.176253 16.691420 -0.970269 2.526302 3.556606 -13.958326 11.634049 0.601352
[712] 0.513944 1.784148 6.963140 13.948034 3.989098 8.552681 -1.819524 -8.605711 10.568255
[721] -8.689244 3.296182 9.347971 9.579683 -4.996927 19.669514 8.214402 21.576987 -3.354634
[730] 16.862911 -7.265758 6.733549 26.113003 2.400829 18.369396 7.820513 -1.301266 8.568795
[739] -4.268840 0.641531 20.325749 19.461683 6.121038 -4.318052 12.716037 2.740823 3.760392
[748] 1.757774 0.017462 7.169518 12.714414 -1.683254 -17.584983 16.546308 30.359976 5.410195
[757] -1.328611 0.983844 17.968225 3.955490 -3.198009 11.150399 17.582791 11.933850 13.303401
[766] 3.346180 -14.141724 15.863480 14.580182 7.442435 -16.832083 15.571718 10.980115 9.057285
[775] 12.223758 22.166146 18.468949 5.159932 -2.596826 0.177323 17.546398 -2.340819 -10.739001
[784] 13.851711 8.649524 -0.676625 1.916790 -0.230504 4.486365 3.497703 -0.974030 -8.302877
[793] -9.957042 15.014614 -5.888460 -4.721529 1.183185 0.888209 16.190886 -1.503265 -13.911138
[802] 11.977848 12.100279 6.292232 -14.023810 -3.759534 -12.159657 1.121957 -6.947301 1.256539
[811] 10.867987 17.357463 8.429953 -1.639224 13.357072 -9.569509 -1.543032 19.295377 14.068746
[820] 14.481696 2.777179 11.448611 -15.947981 11.140153 7.911482 -0.914805 8.657599 9.579986
[829] -1.804865 -9.815804 7.580361 8.306247 2.065076 8.426913 19.803284 -8.565734 12.120181
[838] -2.147138 3.198560 7.143374 10.880711 10.227392 1.402729 0.764356 11.930294 12.809317
[847] -16.394427 2.641887 18.490961 14.833648 -18.337915 7.110666 4.970343 14.513695 13.699080
[856] -7.290834 1.049503 17.622133 -7.504536 -3.722740 8.276255 -5.210400 15.445376 5.436044
[865] -2.251778 7.757196 5.234596 2.733717 27.007857 25.268541 4.065656 11.640994 12.830218
[874] 5.452132 -6.976616 2.417533 -0.062211 3.390345 4.458874 -5.175640 1.479404 6.089584
[883] -12.033990 1.891364 7.365701 25.115510 7.663163 2.599787 1.056858 10.370422 7.933016
[892] 10.187097 2.440983 12.307552 14.815614 2.486874 12.888974 -2.736984 4.362567 2.402114
[901] 9.870973 -25.497400 3.613346 1.818221 14.448089 -7.433200 16.808825 -10.656762 1.349171
[910] -13.792504 11.097103 6.380533 -4.403192 5.192574 3.279144 9.336076 -14.284250 13.425168
[919] 23.041343 3.478552 6.070094 -9.637628 12.883459 8.688329 16.091607 -18.836069 13.398496
[928] 5.240835 11.189526 -1.539839 5.668806 5.575354 -6.911831 17.712669 -2.867567 6.120091
[937] -2.833317 12.702545 -17.544912 10.438811 -18.340939 -3.066357 -3.701081 5.496427 -7.708764
[946] 10.176771 -0.404156 3.491030 9.951289 0.125318 -0.950743 1.744051 -4.739348 -0.859759
[955] -0.234398 10.636048 2.310768 -9.338066 -1.976172 12.334621 19.533671 5.814421 20.861448
[964] -7.207150 12.383274 -2.055910 -2.315557 11.003435 -4.295582 -3.113120 -1.110859 10.263287
[973] 5.595698 -5.453013 5.098184 17.708365 -6.872907 4.302581 4.022909 12.102674 6.063307
[982] 8.300545 13.738784 -7.712485 -2.883992 -3.279923 0.957886 1.273057 0.522583 -14.027584
[991] 1.021488 -8.075856 14.415910 12.664000 13.644429 9.483391 -3.101986 -7.135397 20.942074
[1000] -19.067728
[ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxsynt <- density(pst_gramxsynt)
plot(density_gramxsynt, main = "Density Plot of pst_gramxsynt", xlab = "pst_gramxtyps values", ylab = "Density", col = "red")
pst_gramxgenxlex <- y_posterior$Gram_x_Gen_x_Lex
pst_gramxgenxlex
[1] 0.620247 -14.802705 -3.031907 -12.518206 -3.485587 0.896144 3.107528 -9.625840 -13.807638
[10] -1.854760 -7.190642 -7.530326 0.490543 -1.693198 -5.949240 -12.643799 1.944658 -15.874529
[19] 3.689906 -6.660178 -12.844383 -5.479248 -15.729088 0.009928 -11.152186 -3.481186 -3.005218
[28] -3.888231 -12.468471 -6.519007 -3.542226 7.375220 -6.029863 -2.450335 -20.426812 8.361814
[37] 0.529527 3.802957 -6.831232 3.449239 -13.695477 -1.145402 -2.135542 -8.899339 -0.836994
[46] -0.771729 -2.103416 -7.061291 3.513063 -1.325883 -0.709435 -2.653010 -1.246442 -12.914394
[55] -7.805561 11.540513 -5.995020 -9.074787 -8.644881 -3.211989 7.426858 9.116633 -1.826742
[64] -0.390749 -1.556447 2.437535 5.455385 -14.371282 -11.949033 -0.511778 4.446197 1.861604
[73] 5.296238 -3.349326 12.734896 6.279348 -22.098260 5.885467 0.218703 -9.375201 -6.177203
[82] 7.697090 -3.380415 -14.354835 -7.561720 -13.500687 -5.518803 0.675018 -8.747222 11.838791
[91] -2.414711 1.920746 -3.534129 -2.534596 -9.506622 2.559798 -1.210134 8.740015 -3.730318
[100] -0.122586 0.848465 -1.815424 -7.862387 5.037356 3.227346 -2.880093 -13.556299 -10.254845
[109] -3.132556 -6.643010 7.716824 -0.550454 -9.380685 -21.776045 -9.193138 -8.522697 -1.903133
[118] -10.035891 -9.191380 -5.797914 -14.693970 -8.906908 -8.433130 -17.574949 1.157918 -13.687884
[127] 6.127547 -8.087820 -8.331359 -7.939487 -6.316624 -8.693443 -13.065221 -0.032911 -4.646264
[136] -13.532414 -13.042837 4.320586 -6.099963 5.181466 -16.534965 -8.978441 -5.049911 -3.781763
[145] -6.533067 -11.388316 -4.281225 -7.149702 -0.079692 -8.094843 13.883965 6.765797 -11.511591
[154] -1.593876 -9.263760 -8.760471 3.527212 -11.224924 -10.512672 -9.557601 6.804476 13.843517
[163] -3.770277 12.634594 5.994477 0.637912 -4.788076 -8.656035 -5.239882 -11.096487 -7.157884
[172] -0.940722 -3.697434 0.237897 -5.290765 -6.351777 1.743446 -17.383667 5.192458 -11.139472
[181] -12.132820 -2.259982 1.072937 1.658730 -4.300352 0.312188 -3.566758 -4.117766 -6.701533
[190] -15.218034 -11.657324 -2.316665 3.272414 -3.142184 -10.935175 -3.780663 -6.115197 -7.506022
[199] -4.673553 -4.892705 -11.357990 -3.414878 -17.888750 0.031883 -4.542737 -11.361098 -1.794212
[208] -10.322020 5.493849 -7.137931 -4.120643 -19.275419 -13.902299 2.775271 9.619169 -14.791475
[217] 4.955575 -9.697786 1.360200 -12.115047 -5.850588 2.633057 -3.563479 -14.094379 -15.521000
[226] -12.055339 -15.700789 -6.863448 -1.660808 3.405629 1.177136 -4.861643 -2.159526 -5.515356
[235] -8.027649 3.253884 0.832141 -1.020184 0.832413 14.346098 0.139533 -15.566387 -15.308158
[244] -13.857545 -0.760521 0.968491 -5.041949 -3.920126 6.415560 -1.559519 -13.264340 0.227163
[253] -9.847433 2.747456 1.009345 -6.985124 -7.978682 10.924207 -7.394256 -6.958987 -13.811044
[262] -2.468695 -0.589220 1.245843 3.451556 -4.446727 -1.401428 3.258065 -1.630284 -3.772275
[271] -1.073686 -1.971427 0.819016 12.077074 0.043719 -18.449167 -1.116916 -2.054095 10.499059
[280] 1.414139 -7.979170 -11.205719 -3.404366 7.540113 2.597013 -0.538856 -4.046045 -14.701842
[289] -11.756083 -2.314620 5.827013 -9.898007 -0.337264 -15.585084 -11.739887 3.904751 11.033448
[298] 3.674742 7.910148 -10.836030 -6.354638 -11.746723 -12.538644 -0.464538 -3.512302 -7.818940
[307] -5.347117 -9.747777 2.704066 -6.636801 -4.850151 -0.773624 -9.638883 -4.601407 0.338220
[316] -0.568073 -16.862695 -6.571378 -2.161010 -6.857514 2.674314 -2.807941 7.253726 6.861018
[325] 7.887984 -4.746066 -3.428606 4.525292 -9.257634 -4.931353 15.691386 1.859572 -9.213566
[334] 1.739223 -4.021720 -3.396908 0.154625 -6.964899 -7.912046 -11.010715 3.250941 -4.521081
[343] -11.078600 6.789434 -6.470014 3.413213 -0.918719 -2.771844 -6.666989 -1.341112 -14.986580
[352] 10.804664 -5.575929 -5.016929 -13.003689 -16.685012 -8.112235 -7.279620 3.898139 -6.951367
[361] 0.550031 -4.361964 -10.743320 -11.609663 7.678704 2.556417 -16.102261 8.165762 -3.306431
[370] -5.959541 -19.765213 -11.691467 -2.698805 4.660633 -1.332568 -8.533394 -2.648368 -3.944726
[379] 5.644252 -8.760918 9.131497 -2.281991 0.132333 6.395759 -6.148555 -0.205315 -11.693807
[388] 0.798298 0.297853 -3.428946 1.457795 -6.462318 -9.768840 7.230486 -13.353924 -0.558339
[397] -4.114742 -12.762576 1.326561 -0.582889 -3.092732 -0.701296 -4.695475 -5.043239 4.314954
[406] -6.798533 -14.609792 -2.832972 -16.915572 -6.464366 -1.062764 -5.423406 9.933683 -1.605776
[415] 8.854594 -8.552998 1.246873 3.981942 -17.461225 4.375500 -0.886104 -0.234374 -13.841314
[424] -14.584387 -1.061976 2.030452 -4.701442 -7.712958 -5.425253 -10.510626 -16.268405 -15.952177
[433] -2.722489 -16.425928 -14.459630 -1.417801 -1.403630 -8.529098 -0.173019 -0.901668 -7.299485
[442] 2.146924 -1.518157 -2.488474 -16.883947 -10.479583 -16.197317 -14.640758 13.534771 3.775491
[451] 4.781548 -6.435578 -11.065854 -5.890799 2.348578 -5.801838 -0.222637 -7.981581 -14.688852
[460] -2.648336 13.263132 -23.656847 -6.076678 7.334213 4.761573 -3.294466 -12.659610 -15.464786
[469] -19.903876 -1.075635 -2.510406 4.187656 -0.524530 -19.234291 3.769031 -3.038032 -3.170268
[478] -6.977247 -5.689943 -13.613682 -5.219825 6.138564 -21.760126 -3.512074 -2.348567 -6.430784
[487] -4.208613 10.787802 -3.597537 -6.235739 -0.255110 2.796509 -3.524978 -1.589955 -5.537785
[496] 0.100448 4.507430 -14.027323 -14.718673 -5.032210 3.978935 -8.415242 -7.598378 1.895633
[505] -2.688044 -6.428093 -4.079578 -14.655119 -5.042970 -2.398924 -8.967551 -9.880963 7.086559
[514] -21.782349 -7.402374 4.107432 9.831590 -6.017419 -13.817527 -10.089797 4.159070 2.309493
[523] -11.588469 -21.693938 -1.732658 -15.415267 -5.529209 -10.513573 2.553482 3.458083 -17.926387
[532] -13.505860 -2.921896 -10.185443 -18.835899 -4.003539 -2.009388 -16.684363 -10.872242 -3.111107
[541] -13.939475 -1.871000 -2.494939 -4.722964 8.201888 3.715337 -4.798800 0.258261 -1.044373
[550] -2.750095 -7.466510 -0.223540 -4.301676 -11.833573 -7.805759 -12.561109 -19.545580 -7.381407
[559] 2.678297 -5.632948 -2.656473 -2.637534 4.558934 -10.700316 -2.827868 -11.557701 -3.334698
[568] 0.891357 -10.445311 -6.289334 7.428145 -7.536621 5.639228 0.706883 11.864298 0.475986
[577] -8.269394 5.121606 -8.274295 8.340773 6.578011 3.540447 6.742491 -10.150563 8.266405
[586] 14.895748 2.355100 -13.025111 -11.977957 1.785317 -6.939888 14.294423 -6.984764 -7.309003
[595] -4.470453 -6.653162 -8.122173 -6.006874 -11.890401 3.719309 -6.045160 1.322056 -14.132577
[604] 12.173284 -6.271259 0.027098 2.807536 -2.469541 -12.838001 2.098926 -0.550622 -3.390257
[613] 2.293279 -10.542020 -11.961513 -7.940719 1.524430 -10.606955 7.423870 -9.847959 9.749779
[622] -4.108178 -1.253261 0.504294 -13.007417 -7.862513 -11.729238 -4.161678 -18.402110 -5.371164
[631] 2.952858 -9.793527 1.279108 0.111887 -2.708746 -6.389017 8.564700 -6.090507 -1.982940
[640] -0.056550 -12.234259 -6.980690 -11.297596 2.548561 -3.971944 -6.606962 -1.905842 -6.196378
[649] 6.947096 -12.748760 -1.698266 -4.434041 0.710141 -17.179844 -5.692807 2.992174 -17.428397
[658] -0.703328 -21.199391 1.820945 -16.522415 -7.330906 1.322582 -6.973663 -8.249427 5.551910
[667] -11.164655 -8.355179 -12.989436 -11.239530 -4.962034 -2.523621 6.680256 -4.346330 -1.784670
[676] -9.746036 0.650990 -4.858280 -13.398616 -3.227625 -20.024307 4.928818 2.051492 -9.795434
[685] 1.758038 -7.733281 -5.729714 -0.218997 -2.508496 -12.625296 -4.992942 -11.189414 -5.706230
[694] -14.084361 -13.014068 -0.065583 0.636258 -0.141185 -10.170749 -11.234265 2.953423 1.329771
[703] -11.147428 10.515368 -8.082523 -7.381537 -6.291954 -3.211842 0.015421 -13.156592 -11.252602
[712] -3.962140 -5.167692 -13.131789 -8.307483 -0.241687 -10.897203 -4.320337 -19.007264 -9.577169
[721] -14.657248 5.045017 18.912244 -1.727998 -3.725317 -5.275010 -22.654108 -23.193689 -4.315390
[730] -11.701336 5.594444 3.502378 -17.652708 8.043162 -4.120286 -7.030010 -4.659263 -13.517549
[739] 0.710200 -1.406357 -6.035634 -5.589002 -23.326234 -3.485686 -11.066429 -7.620707 0.700913
[748] 9.560767 4.243233 -2.213589 -4.511416 7.236957 -2.053329 -13.815921 4.200006 0.875215
[757] -14.897697 -1.585937 -12.997388 16.312842 -2.237487 5.670193 -10.471271 -8.923421 -8.081807
[766] -13.640058 -9.687109 -7.663081 -2.202857 -0.378754 -14.335197 -6.224559 -9.500599 -3.842366
[775] 21.742485 -3.833665 0.041075 -12.865040 -18.699726 2.257961 1.244823 -8.128887 3.039698
[784] -11.388324 -0.970902 -18.074340 -0.319527 -7.506121 4.585576 -8.159320 -1.078308 -10.832106
[793] -0.114377 3.316085 -12.055526 -16.876832 -11.402611 -10.071800 -3.832329 -7.967286 7.247344
[802] -8.872758 -4.576918 -5.058670 -12.051726 2.368051 -9.033369 -3.231642 10.854386 -16.697554
[811] 5.347236 7.596276 0.823617 0.481892 -15.743097 14.633736 -7.207215 -8.574181 1.891811
[820] 1.331926 9.720149 -8.724017 6.995028 -12.211293 -8.219172 -0.417589 5.553114 -2.569631
[829] -10.700535 5.074061 -6.909419 -7.107369 -2.292046 3.892487 -13.122155 5.295286 2.367691
[838] -23.526865 -3.160103 1.970745 5.672533 -9.851683 -1.593009 2.264230 -4.184854 -1.724977
[847] -5.957402 5.330426 -7.144102 4.831142 -5.056589 -13.649883 6.151373 0.349801 -16.250039
[856] 0.345436 -7.257334 -7.457765 -1.874905 -6.147608 -14.035478 -2.524430 2.267094 -4.610564
[865] -4.060788 -5.217743 2.820399 -9.386830 -5.061692 -6.484957 -7.391064 -7.197869 -0.749346
[874] -8.427933 -18.900975 5.392020 -13.569570 0.487969 -12.382680 -4.323395 -3.870834 -13.129241
[883] -3.146504 -8.023497 -3.402560 -0.553354 -1.378686 0.870250 1.184436 -10.125064 -1.750484
[892] -13.080093 16.033518 -6.960667 5.849638 -2.272554 -5.575062 1.845667 -9.831104 -14.875070
[901] -8.224598 13.417903 -7.788852 -1.262667 -9.791590 -2.572758 0.453520 -12.982236 -1.728957
[910] 1.275073 -7.771717 -5.098038 -17.639456 5.706840 -4.571080 8.393079 -6.789773 -9.943346
[919] 1.917187 -8.628855 -2.836402 0.030583 -20.947980 3.680629 -17.749991 -5.685082 -8.634569
[928] 11.445404 0.731374 -2.867261 -4.983436 -0.509161 5.714592 -3.955416 -11.137285 -12.551951
[937] -5.851357 2.959546 12.469716 4.355750 13.032723 -13.835589 2.937336 -3.664231 -5.912822
[946] -8.453695 -1.902683 2.053226 -4.158862 -17.211593 2.936978 -9.298263 -10.498156 -8.288327
[955] -12.637861 6.639447 -8.961019 -6.739150 11.067115 -9.019571 -3.377147 -6.240592 -24.084999
[964] -9.665404 11.307511 -11.462502 -7.081446 -5.074047 -6.726880 -15.785986 -7.846104 5.508417
[973] -10.285089 1.042023 -4.616004 -0.390450 -5.443724 -7.790874 -3.705020 -4.215645 2.809870
[982] -7.632529 -15.194150 -1.630142 1.825785 -4.753468 0.171565 -4.908247 4.886107 6.316514
[991] 3.432370 -15.116581 -5.825768 -10.343139 -4.566267 5.458148 -6.620336 12.883634 -16.788879
[1000] -7.662392
[ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxgenxlex <- density(pst_gramxgenxlex)
plot(density_gramxgenxlex, main = "Density Plot of pst_gramxgenxlex", xlab = "pst_gramxgenxlex values", ylab = "Density", col = "red")
pst_gramxgenxsynt <- y_posterior$Gram_x_Gen_x_Synt
pst_gramxgenxsynt
[1] 4.79680 -0.66366 -3.95663 -4.53607 -14.82178 4.06552 -6.11669 -1.67869 -11.10258 -6.52136
[11] -0.25257 13.35644 2.60505 -0.46508 3.55198 -6.75014 9.43359 9.58306 -2.80645 3.93057
[21] -1.64812 -7.75411 7.18887 4.42505 9.85447 12.74543 -9.65229 -0.22764 -3.49437 -12.80035
[31] 4.19057 -12.34624 6.74215 1.19688 4.13037 -14.24327 -13.89912 4.96395 3.73231 5.50308
[41] -1.85232 -10.82491 -7.62924 -8.10522 5.43214 -9.51251 4.44598 -12.86420 7.68843 -18.03039
[51] 0.54491 4.68682 -21.63700 11.51606 7.53301 -15.12835 2.45170 4.69205 1.50995 3.68025
[61] -17.30413 -3.00069 -6.05521 3.53660 -1.28171 3.83999 -2.05078 2.27652 4.53554 -8.74292
[71] 3.06209 -14.22254 -3.87473 2.04190 -14.64583 -1.08543 7.59515 -6.37053 5.03760 4.11445
[81] 5.98911 -8.05926 0.85425 17.32783 -2.16369 -6.00804 -8.36117 -4.54220 0.10815 -3.33501
[91] 0.83863 0.82968 -3.56228 2.27058 16.12058 -9.89188 -0.88719 -12.44936 -7.86365 -10.02252
[101] 0.80040 0.45695 -7.85692 -5.55920 -16.45942 -5.03425 -2.28429 -3.32452 -7.38056 1.79528
[111] -20.40262 -8.00939 11.22045 1.97417 4.56876 -2.59250 0.88417 -1.52974 2.22934 2.44286
[121] 3.08602 -6.75664 -14.59565 -4.20762 -10.80472 16.42758 3.44389 -8.89697 4.43523 0.89889
[131] 5.21822 -0.44863 1.27385 3.91946 -2.55928 -12.59955 6.67246 -3.21958 2.44467 1.04457
[141] 10.48983 4.34259 6.24864 -0.52028 6.11994 -4.77286 -4.16854 5.03682 -5.83879 5.07564
[151] -0.81987 -10.70098 -2.86606 2.60383 -7.95193 4.86093 -4.83333 -15.83810 -12.07366 -5.20263
[161] 7.44624 -16.31818 -3.09645 -16.26770 -10.79059 -3.05172 -14.59105 -2.87476 1.08893 -4.14071
[171] 14.25532 -12.81575 3.44824 3.98420 0.24049 2.03289 -4.60779 -5.40431 -0.08470 3.37931
[181] 0.79936 -2.33568 -3.45137 1.38088 -1.48262 6.91184 3.42439 17.58672 -4.54290 14.16279
[191] 3.41243 -15.50567 -7.90779 18.78620 1.44027 21.15618 -0.08364 -6.43235 -3.23267 -11.37175
[201] 11.66050 6.41293 8.22507 0.80143 3.01132 6.56451 12.18455 11.04810 -11.89542 10.32697
[211] 4.92790 -1.01939 0.84898 1.48878 -17.26581 -12.26544 -1.69592 2.97373 -3.36433 13.38861
[221] -1.22144 4.56913 -6.18935 6.79547 5.88359 5.76383 4.95729 -2.77201 -3.85408 -1.69244
[231] 1.30238 0.04024 1.63689 -8.32265 9.19778 -9.07617 2.03961 0.90842 4.32316 -9.33032
[241] 7.10762 2.40833 0.33314 -1.06807 -3.46095 -13.33281 8.71181 10.53903 7.64933 -8.07725
[251] -5.74637 -8.68838 12.50080 -17.49129 0.38741 11.44297 -8.35917 -4.68873 0.78127 0.80676
[261] -1.00959 4.70160 8.45713 2.51500 -2.72119 7.64757 -4.76983 -7.26742 1.05975 -2.00104
[271] 0.59734 -3.54924 -17.41918 -10.93745 4.24897 20.16249 -4.38695 -0.30389 5.61923 -1.08673
[281] 5.73644 15.96977 -2.29246 -19.48236 -9.35489 1.23592 8.91882 14.62063 9.07823 -2.54926
[291] -6.45629 3.27365 3.47729 -4.23807 9.52990 -2.99231 -14.37449 -14.74917 3.16050 -0.72516
[301] -9.34162 12.01793 2.65525 4.21371 1.73474 -2.06606 -8.63286 -6.96728 2.49832 -3.37411
[311] -6.97259 -0.40974 11.99833 -1.03509 2.21754 -15.24405 6.60547 -1.55384 -3.62616 2.89877
[321] -7.88314 12.55398 -9.74817 -7.33908 -6.84247 -0.65540 15.06684 0.88349 2.55417 2.80819
[331] -7.80754 1.43274 -0.13708 6.27036 1.56301 -6.72339 3.07820 -1.86643 -8.11209 -4.98292
[341] -4.53138 2.54747 9.45418 -1.04300 -2.88764 -0.84772 7.28758 -7.35568 -3.62747 -11.94777
[351] 5.85402 -6.69356 4.42359 -3.06494 -2.09807 6.35945 3.08040 7.60341 -0.38057 -15.67417
[361] -4.01967 0.51797 3.93051 11.88993 -3.82948 -3.93018 -6.57109 3.48874 3.86306 -3.43056
[371] 2.90849 -4.74979 2.78482 -0.21962 -1.24498 -1.03178 -2.81645 3.17303 0.74535 -5.97158
[381] -14.38914 -7.55005 -8.72105 -7.75295 0.82800 -3.85458 11.13218 -13.45006 -2.15249 7.24786
[391] 5.70858 11.15627 -0.10056 -6.62993 -2.99426 3.70457 7.52683 9.13529 -4.75849 -1.11798
[401] 8.50455 -9.35037 1.89316 -11.36686 -3.09600 2.54759 6.05223 4.69461 -3.78791 -15.74405
[411] -11.24229 1.50943 -11.94883 3.73002 -10.81085 -6.92926 -14.72166 -4.68388 -0.86850 -14.42751
[421] 13.77758 -15.15254 1.60323 1.32665 10.84280 4.17600 5.54015 -1.92205 0.23147 -0.83056
[431] -5.33514 -4.37006 6.64579 -9.30778 -4.95591 -4.53308 4.75256 1.33908 4.61772 -8.62356
[441] 4.56283 -12.59240 -1.31703 -0.36174 4.60961 -1.07131 1.04040 -4.08906 -12.06296 6.30353
[451] 3.48078 -1.11259 -6.39971 -0.35451 -8.98864 1.59138 -3.50060 -11.71642 10.43516 8.18201
[461] -14.58988 4.91710 -7.22259 -2.63883 -4.84113 -0.38368 0.74348 1.57662 -6.37795 9.08419
[471] -4.88120 -9.80800 2.75498 19.11648 -6.75040 0.61883 5.02318 -0.43750 -9.71998 4.55130
[481] 4.65533 -5.95557 -1.64783 -8.95818 -11.83004 5.93306 5.08460 -16.00421 -2.47398 7.13274
[491] -8.62462 -4.26217 -10.50634 -6.34045 7.27456 -10.62809 5.30840 -4.74419 -4.76206 -3.74513
[501] 4.30048 8.37244 -0.61434 -6.82440 -1.33141 4.52402 13.14806 -7.23795 -11.04717 1.27587
[511] 7.85785 7.15648 -4.62579 8.79503 8.59337 0.13336 -6.58775 -3.35582 -3.78230 9.11007
[521] -4.32392 0.97297 1.95285 10.15643 4.41570 9.45688 -0.03124 -2.91259 -7.28229 -5.23713
[531] 5.09551 13.69087 -6.15977 4.55086 -3.77403 -0.79941 3.65810 8.36591 -2.02657 -2.08390
[541] 16.68501 0.66901 -7.79278 -4.69246 -14.12354 -6.02752 7.47830 -8.29074 -9.28136 8.18579
[551] 0.69957 5.71629 -3.24319 -4.03836 4.66481 11.02112 15.17714 -8.18491 14.60219 -8.37051
[561] -0.99550 -10.31264 -0.61431 -1.03096 -5.04767 2.48936 12.98982 -1.93032 -1.56462 -1.24008
[571] -9.08483 3.98283 -17.34103 -12.39267 -3.44971 -2.68159 1.51564 2.20357 -12.29948 9.80752
[581] -14.37618 2.80384 -5.00508 6.20246 -11.69552 -3.90801 -8.63978 4.46300 7.09745 -4.15487
[591] 2.86024 -3.20448 -8.56900 7.81890 -2.72164 0.85900 -15.02990 -1.17635 -1.28095 5.78900
[601] 1.37368 8.29555 -7.47849 2.42205 2.68630 -12.52266 -10.68540 5.87396 20.15916 -19.09497
[611] 3.40496 4.03689 6.96458 13.44747 -0.84731 1.83747 -7.55059 9.95183 -15.07585 0.16447
[621] 0.47122 3.57319 -0.57295 0.85751 6.31198 -7.64016 -1.20628 -10.47506 2.81206 -2.31528
[631] -2.18538 6.96322 3.85904 -3.08985 -0.78018 -0.44074 13.31577 -2.09426 2.37098 -6.95222
[641] 11.54578 -2.28373 3.58547 2.83131 -6.96746 1.35476 -2.82577 -5.30719 -5.15738 -3.53513
[651] -2.27071 -10.81718 2.52873 10.78710 0.58995 -3.77394 23.90205 16.96941 3.63778 -2.37676
[661] 4.14784 -1.45876 -0.51433 5.25121 -4.85009 1.68906 12.07853 9.17475 -13.22617 -5.58723
[671] 4.53172 -8.36025 -12.79123 -3.94993 -3.17967 2.13141 0.04840 -0.92287 7.18717 -2.97405
[681] -1.87123 5.59712 4.57467 -1.25091 -8.36981 -4.14314 -3.03748 -3.68798 -1.76825 5.05743
[691] 7.01455 10.62312 3.27583 7.43941 -7.90156 -2.49159 -6.62102 11.19564 15.33847 2.27724
[701] -0.65882 3.85682 3.41269 -4.56832 -6.15672 0.20299 10.66067 9.94390 -5.38442 -1.89620
[711] 2.93187 -19.09909 6.09052 -4.67877 21.36651 -0.97886 3.55853 0.19822 1.28536 -1.12138
[721] -3.15654 -9.62092 -6.49594 7.67597 3.20844 13.17165 -9.12641 7.07396 5.16062 5.17990
[731] -10.21281 -3.84346 18.85390 -16.67855 -11.72858 -2.01287 -14.50355 10.61636 -0.50898 -5.53342
[741] -3.16896 -5.39847 -0.30850 5.14996 8.68014 -11.91333 1.77128 6.94479 -15.46782 -7.30382
[751] 5.19234 -0.62978 -10.11059 13.35301 -9.94152 6.53848 5.10064 -2.19794 11.87526 -7.81431
[761] 4.22935 5.66007 -9.10613 2.27731 -2.38874 0.75511 5.04614 -5.88689 -4.47708 -6.93521
[771] 4.62171 -2.98139 -6.55029 1.35318 -2.26634 1.23321 -2.42234 -5.58933 7.74951 -2.52626
[781] -21.98034 -9.07733 6.65731 5.89711 -0.67201 14.94723 -1.72227 1.28336 -2.55218 6.27036
[791] -7.55694 -10.05438 2.33089 -6.47918 14.38109 7.97685 13.43863 5.72975 -6.27088 -5.12891
[801] -10.38483 -2.65587 -0.17726 1.93406 10.61523 12.77508 -3.30400 -1.52994 -8.36522 -2.50204
[811] -14.87248 1.15320 -15.47669 3.64903 -3.51109 3.38369 -7.70409 -9.33328 -9.56918 -11.96660
[821] -13.59116 3.75524 -15.06012 -3.21806 5.01985 2.79399 -6.09129 -3.89004 5.43588 -10.99740
[831] 0.62360 -2.50204 8.59965 -5.11086 16.02439 -15.36392 1.07489 -5.70692 -11.92035 -9.38351
[841] -15.00852 5.40399 -1.38646 -9.20040 -3.10081 -5.03051 -1.81434 -12.80361 -1.57690 -0.82307
[851] -11.53819 -4.31601 0.99441 4.08846 3.01525 -8.74020 12.03497 14.78479 -2.50670 3.19120
[861] -11.08006 1.42757 -8.86845 -8.02188 6.35772 1.23401 -17.15380 2.29389 8.58714 4.69361
[871] -1.30780 -2.87619 -9.44154 -9.48911 6.19711 -3.78805 3.14122 5.54141 -6.09216 7.33715
[881] -7.71926 4.34560 -3.47014 0.26588 -5.69945 -2.89963 -1.61871 -16.69608 -0.15318 -3.50287
[891] 10.97544 9.04545 7.95043 -2.78155 6.39848 -0.80594 -6.93367 -12.01238 -6.13132 -2.03949
[901] -6.47285 -15.51588 -1.01739 -4.42227 5.38898 3.56857 -4.56961 11.97815 -17.70165 -5.39138
[911] 18.09184 -8.50665 13.94961 0.69198 -2.35002 1.94383 0.34171 -7.21741 0.20248 -3.41240
[921] -0.92581 4.21961 5.49025 -15.20761 8.80558 -0.14488 -7.59720 -11.04855 -11.52112 1.39354
[931] 11.44962 -1.62357 -9.56391 5.19771 2.46518 14.23173 -1.50831 -1.03227 -3.22814 -1.73574
[941] -18.99407 -0.95427 -5.46847 -3.97505 -17.13381 3.04102 -1.84459 -4.29846 -8.07855 7.89818
[951] -11.65176 9.96206 1.10124 3.18137 1.55294 -9.91283 -2.05961 11.92111 -2.51286 7.59277
[961] -18.84041 -2.71859 12.91002 -1.14595 -9.13255 -2.63204 -7.55096 -0.76148 4.96732 4.45690
[971] -8.44975 -1.66066 -2.84893 -10.61449 -1.30864 4.70363 5.29430 -10.89069 9.64045 1.55495
[981] -8.34681 -8.32832 -0.16497 11.22528 0.58928 -4.96090 -7.47640 10.39034 -5.72071 -2.39909
[991] 0.03738 -7.35270 1.91139 -12.91750 7.07267 -15.27261 -8.61871 1.12946 -1.71334 -3.11270
[ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxgenxsynt <- density(pst_gramxgenxsynt)
plot(density_gramxgenxsynt, main = "Density Plot of pst_gramxgenxsynt", xlab = "pst_gramxgenxsynt values", ylab = "Density", col = "red")
# check posterior predicts --> the fits looks very good --> suspision of overfit? --> cv validation?
predicts <- y_posterior$Predict_rt
dim(predicts) # 8000 x 1271
[1] 8000 1771
y_true <- contr_et %>%
filter(AOI_id == region) %>% # Filter by the specified region
filter(!is.na(.data[[meas]])) %>% # Filter out NA values for the specific measure
pull(.data[[meas]])
ppc_dens_overlay(y_true, yrep = predicts[1:200, ])
stats_df <- data.frame()
# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
for (meth in methods) {
# Loop over each measure type to read the corresponding model and extract data
for (region in regions) {
for (meas in measure_types) {
model_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
m1 <- readRDS(model_path)
# print(summary(m1))
# Extract posterior distributions
y_posterior <- extract(m1)
intercept <- exp(y_posterior$beta[,1])
betas <- c("b_0", "b_Gram", "b_Gen","b_Synt", "b_Lex",
"b_Gram_x_Synt", "b_Gram_x_Lex", "b_Gram_x_Gen_x_Synt", "b_Gram_x_Gen_x_Lex")
posterior_samples <- list(intercept, y_posterior$Gram, y_posterior$Gen, y_posterior$Synt, y_posterior$Lex,
y_posterior$Gram_x_Synt, y_posterior$Gram_x_Lex, y_posterior$Gram_x_Gen_x_Synt, y_posterior$Gram_x_Gen_x_Lex)
hpdi_95 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.95))
hpdi_89 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.89))
# Prepare the results data frame
temp_results <- data.frame(
method = rep(meth, length(betas)),
region = rep(region, length(betas)),
measure = rep(meas, length(betas)),
beta = betas,
bval_mean = sapply(posterior_samples, mean),
crI_95_lower = sapply(posterior_samples, function(x) quantile(x, 0.025)),
crI_95_upper = sapply(posterior_samples, function(x) quantile(x, 0.975)),
crl_89_lower = sapply(posterior_samples, function(x) quantile(x, 0.055)),
crl_89_upper = sapply(posterior_samples, function(x) quantile(x, 0.945)),
hpdi_95_lower = sapply(hpdi_95, function(x) x[1]),
hpdi_95_upper = sapply(hpdi_95, function(x) x[2]),
hpdi_89_lower = sapply(hpdi_89, function(x) x[1]),
hpdi_89_upper = sapply(hpdi_89, function(x) x[2]),
bval_median = sapply(posterior_samples, median)
)
# Append the temp_results to the stats_df data frame
stats_df <- rbind(stats_df, temp_results)
}
}
}
# View(stats_df)
stats_df <- stats_df %>%
mutate(across(
where(is.numeric),
~ if_else(measure %in% c("FPReg", "RegIn_incl"), round(., 3), round(., 0))
)) %>%
mutate(
annotation = paste0(round(bval_mean, 2),
" [", round(crI_95_lower, 2), ", ",
round(crI_95_upper, 2), "]")
)
write.csv(stats_df, "./stats2/stats_bayesian.csv", row.names = FALSE)
regions <- c("R2", "R3", "R4", "R5")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
groups <- c("gender_match", "target_gender", "lex", "synt")
all_diffs <- data.frame()
# Loop through each region, measure type, and group
for (region in regions) {
for (meas in measure_types) {
for (group in groups) {
# Calculate mean and difference for each subgroup
summary_stats <- contr_motr %>%
mutate(lex = if_else(type=="stim_verb", "v", "a"),
synt = if_else(type=="stim_adj", "in", "ex")) %>%
filter(AOI_id == region) %>% # Filter by region
filter(!is.na(.data[[meas]])) %>% # Filter out NA values for measure
group_by(.data[[group]]) %>% # Group by current group variable
summarise(mean_value = mean(.data[[meas]], na.rm = TRUE)) %>% # Mean of measure
summarise(diff = diff(mean_value)) # Difference between the means
# Append the results to all_diffs
all_diffs <- rbind(all_diffs,
data.frame(
region = region,
measure_type = meas,
group = group,
diff = summary_stats$diff
))
}
}
}
all_diffs
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# prepare motr for plotting
motr_plot <- contr_motr %>%
dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
) %>%
drop_na(total_duration) %>%
gather(measure, value, measure_types) %>%
filter(region %in%c(2, 3, 4, 5)) %>%
drop_na()
# View(motr_plot)
motr_lex <- motr_plot %>%
group_by(lex, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(lex, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(lex, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Lexical Category",
method = "MoTR") %>%
rename(type = lex)
motr_synt <- motr_plot %>%
group_by(synt, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(synt, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(synt, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Agreement Type",
method = "MoTR") %>%
rename(type = synt)
# plot et data for plotting
et_plot <- contr_et %>%
dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
) %>%
drop_na(total_duration) %>%
gather(measure, value, measure_types) %>%
filter(region %in%c(2, 3, 4, 5)) %>%
drop_na()
et_lex <- et_plot %>%
group_by(lex, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(lex, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(lex, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Lexical Category",
method = "Eye-tr.") %>%
rename(type = lex)
et_synt <- et_plot %>%
group_by(synt, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(synt, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(synt, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Agreement Type",
method = "Eye-tr.") %>%
rename(type = synt)
motr_et_plot <- rbind(motr_lex, motr_synt, et_lex, et_synt)
stats_df <- read_csv("./stats/stats_bayesian.csv", show_col_types = FALSE)
annotation <- stats_df %>%
mutate(region = as.double(substr(region, 2, 2)),
measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob.")),
method = if_else(method=="motr", "MoTR", "Eye-tr."))%>%
filter(beta %in% c("b_Gram_x_Lex", "b_Gram_x_Synt")) %>%
mutate(Prediction = if_else(beta == "b_Gram_x_Lex", "Lexical Category", "Agreement Type")) %>%
dplyr::select(method, region, measure, Prediction, annotation)
View(annotation)
plot_annotated <- motr_et_plot %>%
left_join(annotation, by = c("method", "region", "measure", "Prediction")) %>%
mutate(annotation = if_else(is.na(annotation), "", annotation)) %>%
mutate(annotation = if_else(type %in% c("Verb", "External"), "", annotation))
plot_annotated
plot_annotated %>%
filter(method == "MoTR") %>%
filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point(aes(shape = type)) +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Reading time difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"External" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "MoTR",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = guide_legend(
title = "MoTR",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid")
)
)
)
Warning: Duplicated `override.aes` is ignored.
ggsave(paste0("./images/motr_rt_interaction.pdf"), device="pdf", height=6, width=8)
Warning: Duplicated `override.aes` is ignored.
plot_annotated %>%
filter(method == "MoTR") %>%
filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point() +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Regression prob. difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"Agreement" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = "none"
)
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
ggsave(paste0("./images/motr_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
plot_annotated %>%
filter(method == "Eye-tr.") %>%
filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point(aes(shape = type)) +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Reading time difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"External" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid")
)
)
)
Warning: Duplicated `override.aes` is ignored.
ggsave(paste0("./images/et_rt_interaction.pdf"), device="pdf", height=6, width=8)
Warning: Duplicated `override.aes` is ignored.
plot_annotated %>%
filter(method == "Eye-tr.") %>%
filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point() +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Regression prob. difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"Agreement" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = "none"
)
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
ggsave(paste0("./images/et_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).